Converting content from a first to a second aptitude level

ABSTRACT

A method performed by a computing device includes generating a first aptitude level entigen group for a first aptitude level phrase in accordance with identigen rules. The first aptitude level entigen group represents a most likely interpretation of the first aptitude level phrase. The method further includes obtaining a multiple aptitude level entigen group from a knowledge database based on the first aptitude level entigen group. The multiple aptitude level entigen group includes the first aptitude level entigen group. The method further includes generating a second aptitude level entigen group utilizing the multiple aptitude level entigen group. The method further includes generating a second aptitude level phrase based on the second aptitude level entigen group. The second aptitude level entigen group represents a most likely interpretation of the second aptitude level phrase.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/797,454,entitled “TRANSLATING CONTENT FROM A FIRST TO A SECOND APTITUDE LEVEL,”filed Jan. 28, 2019, which is hereby incorporated herein by reference inits entirety and made part of the present U.S. Utility PatentApplication for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not Applicable

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computing systems and moreparticularly to generating data representations of data and analyzingthe data utilizing the data representations.

Description of Related Art

It is known that data is stored in information systems, such as filescontaining text. It is often difficult to produce useful informationfrom this stored data due to many factors. The factors include thevolume of available data, accuracy of the data, and variances in howtext is interpreted to express knowledge. For example, many languagesand regional dialects utilize the same or similar words to representdifferent concepts.

Computers are known to utilize pattern recognition techniques and applystatistical reasoning to process text to express an interpretation in anattempt to overcome ambiguities inherent in words. One patternrecognition technique includes matching a word pattern of a query to aword pattern of the stored data to find an explicit textual answer.Another pattern recognition technique classifies words into majorgrammatical types such as functional words, nouns, adjectives, verbs andadverbs. Grammar based techniques then utilize these grammatical typesto study how words should be distributed within a string of words toform a properly constructed grammatical sentence where each word isforced to support a grammatical operation without necessarilyidentifying what the word is actually trying to describe.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a computingsystem in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of various serversof a computing system in accordance with the present invention;

FIG. 3 is a schematic block diagram of an embodiment of various devicesof a computing system in accordance with the present invention;

FIGS. 4A and 4B are schematic block diagrams of another embodiment of acomputing system in accordance with the present invention;

FIG. 4C is a logic diagram of an embodiment of a method for interpretingcontent to produce a response to a query within a computing system inaccordance with the present invention;

FIG. 5A is a schematic block diagram of an embodiment of a collectionsmodule of a computing system in accordance with the present invention;

FIG. 5B is a logic diagram of an embodiment of a method for obtainingcontent within a computing system in accordance with the presentinvention;

FIG. 5C is a schematic block diagram of an embodiment of a query moduleof a computing system in accordance with the present invention;

FIG. 5D is a logic diagram of an embodiment of a method for providing aresponse to a query within a computing system in accordance with thepresent invention;

FIG. 5E is a schematic block diagram of an embodiment of an identigenentigen intelligence (WI) module of a computing system in accordancewith the present invention;

FIG. 5F is a logic diagram of an embodiment of a method for analyzingcontent within a computing system in accordance with the presentinvention;

FIG. 6A is a schematic block diagram of an embodiment of an elementidentification module and an interpretation module of a computing systemin accordance with the present invention;

FIG. 6B is a logic diagram of an embodiment of a method for interpretinginformation within a computing system in accordance with the presentinvention;

FIG. 6C is a schematic block diagram of an embodiment of an answerresolution module of a computing system in accordance with the presentinvention;

FIG. 6D is a logic diagram of an embodiment of a method for producing ananswer within a computing system in accordance with the presentinvention;

FIG. 7A is an information flow diagram for interpreting informationwithin a computing system in accordance with the present invention;

FIG. 7B is a relationship block diagram illustrating an embodiment ofrelationships between things and representations of things within acomputing system in accordance with the present invention;

FIG. 7C is a diagram of an embodiment of a synonym words table within acomputing system in accordance with the present invention;

FIG. 7D is a diagram of an embodiment of a polysemous words table withina computing system in accordance with the present invention;

FIG. 7E is a diagram of an embodiment of transforming words intogroupings within a computing system in accordance with the presentinvention;

FIG. 8A is a data flow diagram for accumulating knowledge within acomputing system in accordance with the present invention;

FIG. 8B is a diagram of an embodiment of a groupings table within acomputing system in accordance with the present invention;

FIG. 8C is a data flow diagram for answering questions utilizingaccumulated knowledge within a computing system in accordance with thepresent invention;

FIG. 8D is a data flow diagram for answering questions utilizinginterference within a computing system in accordance with the presentinvention;

FIG. 8E is a relationship block diagram illustrating another embodimentof relationships between things and representations of things within acomputing system in accordance with the present invention;

FIGS. 8F and 8G are schematic block diagrams of another embodiment of acomputing system in accordance with the present invention;

FIG. 8H is a logic diagram of an embodiment of a method for processingcontent to produce knowledge within a computing system in accordancewith the present invention;

FIGS. 8J and 8K are schematic block diagrams another embodiment of acomputing system in accordance with the present invention;

FIG. 8L is a logic diagram of an embodiment of a method for generating aquery response to a query within a computing system in accordance withthe present invention;

FIGS. 9A and 9B are schematic block diagrams of another embodiment of acomputing system illustrating a method for converting content of a firstaptitude level to converted content of a second aptitude level withinthe computing system in accordance with the present invention.

FIG. 10A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 10B is a data flow diagram of an embodiment of formalizing contentwithin a computing system in accordance with the present invention;

FIG. 10C is a logic diagram of an embodiment of a method for formalizingcontent within a computing system in accordance with the presentinvention;

FIG. 11A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 11B is structure diagram of an embodiment of a knowledge databasewithin a computing system in accordance with the present invention;

FIG. 11C is a logic diagram of an embodiment of a method for accessing aknowledge database within a computing system in accordance with thepresent invention;

FIG. 12A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 12B is a structure diagram of another embodiment of a knowledgedatabase within a computing system in accordance with the presentinvention;

FIG. 12C is a logic diagram of another embodiment of a method foraccessing a knowledge database within a computing system in accordancewith the present invention;

FIG. 13A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 13B is a data flow diagram of an embodiment for correcting impairedcontent within a computing system in accordance with the presentinvention; and

FIG. 13C is a logic diagram of an embodiment of a method for correctingimpaired content within a computing system in accordance with thepresent invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a computingsystem 10 that includes a plurality of user devices 12-1 through 12-N, aplurality of wireless user devices 14-1 through 14-N, a plurality ofcontent sources 16-1 through 16-N, a plurality of transactional servers18-1 through 18-N, a plurality of artificial intelligence (AI) servers20-1 through 20-N, and a core network 24. The core network 24 includesat least one of the Internet, a public radio access network (RAN), andany private network. Hereafter, the computing system 10 may beinterchangeably referred to as a data network, a data communicationnetwork, a system, a communication system, and a data communicationsystem. Hereafter, the user device and the wireless user device may beinterchangeably referred to as user devices, and each of thetransactional servers and the AI servers may be interchangeably referredto as servers.

Each user device, wireless user device, transactional server, and AIserver includes a computing device that includes a computing core. Ingeneral, a computing device is any electronic device that cancommunicate data, process data, and/or store data. A further generalityof a computing device is that it includes one or more of a centralprocessing unit (CPU), a memory system, a sensor (e.g., internal orexternal), user input/output interfaces, peripheral device interfaces,communication elements, and an interconnecting bus structure.

As further specific examples, each of the computing devices may be aportable computing device and/or a fixed computing device. A portablecomputing device may be an embedded controller, a smart sensor, a smartpill, a social networking device, a gaming device, a cell phone, a smartphone, a robot, a personal digital assistant, a digital music player, adigital video player, a laptop computer, a handheld computer, a tablet,a video game controller, an engine controller, a vehicular controller,an aircraft controller, a maritime vessel controller, and/or any otherportable device that includes a computing core. A fixed computing devicemay be security camera, a sensor device, a household appliance, amachine, a robot, an embedded controller, a personal computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a cameracontroller, a video game console, a critical infrastructure controller,and/or any type of home or office computing equipment that includes acomputing core. An embodiment of the various servers is discussed ingreater detail with reference to FIG. 2. An embodiment of the variousdevices is discussed in greater detail with reference to FIG. 3.

Each of the content sources 16-1 through 16-N includes any source ofcontent, where the content includes one or more of data files, a datastream, a tech stream, a text file, an audio stream, an audio file, avideo stream, a video file, etc. Examples of the content sources includea weather service, a multi-language online dictionary, a fact server, abig data storage system, the Internet, social media systems, an emailserver, a news server, a schedule server, a traffic monitor, a securitycamera system, audio monitoring equipment, an information server, aservice provider, a data aggregator, and airline traffic server, ashipping and logistics server, a banking server, a financial transactionserver, etc. Alternatively, or in addition to, one or more of thevarious user devices may provide content. For example, a wireless userdevice may provide content (e.g., issued as a content message) when thewireless user device is able to capture data (e.g., text input, sensorinput, etc.).

Generally, an embodiment of this invention presents solutions where thecomputing system 10 supports the generation and utilization of knowledgeextracted from content. For example, the AI servers 20-1 through 20-Ningest content from the content sources 16-1 through 16-N by receiving,via the core network 24 content messages 28-1 through 28-N as AImessages 32-1 through 32-N, extract the knowledge from the ingestedcontent, and interact with the various user devices to utilize theextracted knowledge by facilitating the issuing, via the core network24, user messages 22-1 through 22-N to the user devices 12-1 through12-N and wireless signals 26-1 through 26-N to the wireless user devices14-1 through 14-N.

Each content message 28-1 through 28-N includes a content request (e.g.,requesting content related to a topic, content type, content timing, oneor more domains, etc.) or a content response, where the content responseincludes real-time or static content such as one or more of dictionaryinformation, facts, non-facts, weather information, sensor data, newsinformation, blog information, social media content, user daily activityschedules, traffic conditions, community event schedules, schoolschedules, user schedules airline records, shipping records, logisticsrecords, banking records, census information, global financial historyinformation, etc. Each AI message 32-1 through 32-N includes one or moreof content messages, user messages (e.g., a query request, a queryresponse that includes an answer to a query request), and transactionmessages (e.g., transaction information, requests and responses relatedto transactions). Each user message 22-1 through 22-N includes one ormore of a query request, a query response, a trigger request, a triggerresponse, a content collection, control information, softwareinformation, configuration information, security information, routinginformation, addressing information, presence information, analyticsinformation, protocol information, all types of media, sensor data,statistical data, user data, error messages, etc.

When utilizing a wireless signal capability of the core network 24, eachof the wireless user devices 14-1 through 14-N encodes/decodes dataand/or information messages (e.g., user messages such as user messages22-1 through 22-N) in accordance with one or more wireless standards forlocal wireless data signals (e.g., Wi-Fi, Bluetooth, ZigBee) and/or forwide area wireless data signals (e.g., 2G, 3G, 4G, 5G, satellite,point-to-point, etc.) to produce wireless signals 26-1 through 26-N.Having encoded/decoded the data and/or information messages, thewireless user devices 14-1 through 14-N and/receive the wireless signalsto/from the wireless capability of the core network 24.

As another example of the generation and utilization of knowledge, thetransactional servers 18-1 through 18-N communicate, via the corenetwork 24, transaction messages 30-1 through 30-N as further AImessages 32-1 through 32-N to facilitate ingesting of transactional typecontent (e.g., real-time crypto currency transaction information) and tofacilitate handling of utilization of the knowledge by one or more ofthe transactional servers (e.g., for a transactional function) inaddition to the utilization of the knowledge by the various userdevices. Each transaction message 30-1 through 30-N includes one or moreof a query request, a query response, a trigger request, a triggerresponse, a content message, and transactional information, where thetransactional information may include one or more of consumer purchasinghistory, crypto currency ledgers, stock market trade information, otherinvestment transaction information, etc.

In another specific example of operation of the generation andutilization of knowledge extracted from the content, the user device12-1 issues a user message 22-1 to the AI server 20-1, where the usermessage 22-1 includes a query request and where the query requestincludes a question related to a first domain of knowledge. The issuingincludes generating the user message 22-1 based on the query request(e.g., the question), selecting the AI server 20-1 based on the firstdomain of knowledge, and sending, via the core network 24, the usermessage 22-1 as a further AI message 32-1 to the AI server 20-1. Havingreceived the AI message 32-1, the AI server 20-1 analyzes the questionwithin the first domain, generates further knowledge, generates apreliminary answer, generates a quality level indicator of thepreliminary answer, and determines to gather further content when thequality level indicator is below a minimum quality threshold level. Whengathering the further content, the AI server 20-1 issues, via the corenetwork 24, a still further AI message 32-1 as a further content message28-1 to the content source 16-1, where the content message 28-1 includesa content request for more content associated with the first domain ofknowledge and in particular the question. Alternatively, or in additionto, the AI server 20-1 issues the content request to another AI serverto facilitate a response within a domain associated with the other AIserver. Further alternatively, or in addition to, the AI server 20-1issues the content request to one or more of the various user devices tofacilitate a response from a subject matter expert.

Having received the content message 28-1, the contents or 16-1 issues,via the core network 24, a still further content message 28-1 to the AIserver 20-1 as a yet further AI message 32-1, where the still furthercontent message 28-1 includes requested content. The AI server 20-1processes the received content to generate further knowledge. Havinggenerated the further knowledge, the AI server 20-1 re-analyzes thequestion, generates still further knowledge, generates anotherpreliminary answer, generates another quality level indicator of theother preliminary answer, and determines to issue a query response tothe user device 12-1 when the quality level indicator is above theminimum quality threshold level. When issuing the query response, the AIserver 20-1 generates an AI message 32-1 that includes another usermessage 22-1, where the other user message 22-1 includes the otherpreliminary answer as a query response including the answer to thequestion. Having generated the AI message 32-1, the AI server 20-1sends, via the core network 24, the AI message 32-1 as the user message22-1 to the user device 12-1 thus providing the answer to the originalquestion of the query request.

FIG. 2 is a schematic block diagram of an embodiment of the AI servers20-1 through 20-N and the transactional servers 18-1 through 18-N of thecomputing system 10 of FIG. 1. The servers include a computing core 52,one or more visual output devices 74 (e.g., video graphics display,touchscreen, LED, etc.), one or more user input devices 76 (e.g.,keypad, keyboard, touchscreen, voice to text, a push button, amicrophone, a card reader, a door position switch, a biometric inputdevice, etc.), one or more audio output devices 78 (e.g., speaker(s),headphone jack, a motor, etc.), and one or more visual input devices 80(e.g., a still image camera, a video camera, photocell, etc.).

The servers further include one or more universal serial bus (USB)devices (USB devices 1-U), one or more peripheral devices (e.g.,peripheral devices 1-P), one or more memory devices (e.g., one or moreflash memory devices 92, one or more hard drive (HD) memories 94, andone or more solid state (SS) memory devices 96, and/or cloud memory 98).The servers further include one or more wireless location modems 84(e.g., global positioning satellite (GPS), Wi-Fi, angle of arrival, timedifference of arrival, signal strength, dedicated wireless location,etc.), one or more wireless communication modems 86-1 through 86-N(e.g., a cellular network transceiver, a wireless data networktransceiver, a Wi-Fi transceiver, a Bluetooth transceiver, a 315 MHztransceiver, a zig bee transceiver, a 60 GHz transceiver, etc.), a telcointerface 102 (e.g., to interface to a public switched telephonenetwork), and a wired local area network (LAN) 88 (e.g., optical,electrical), and a wired wide area network (WAN) 90 (e.g., optical,electrical).

The computing core 52 includes a video graphics module 54, one or moreprocessing modules 50-1 through 50-N (e.g., which may include one ormore secure co-processors), a memory controller 56 and one or more mainmemories 58-1 through 58-N (e.g., RAM serving as local memory). Thecomputing core 52 further includes one or more input/output (I/O) deviceinterfaces 62, an input/output (I/O) controller 60, a peripheralinterface 64, one or more USB interfaces 66, one or more networkinterfaces 72, one or more memory interfaces 70, and/or one or moreperipheral device interfaces 68.

The processing modules may be a single processing device or a pluralityof processing devices where the processing device may further bereferred to as one or more of a “processing circuit”, a “processor”,and/or a “processing unit”. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions.

The processing module, module, processing circuit, and/or processingunit may be, or further include, memory and/or an integrated memoryelement, which may be a single memory device, a plurality of memorydevices, and/or embedded circuitry of another processing module, module,processing circuit, and/or processing unit. Such a memory device may bea read-only memory, random access memory, volatile memory, non-volatilememory, static memory, dynamic memory, flash memory, cache memory,and/or any device that stores digital information. Note that if theprocessing module, module, processing circuit, and/or processing unitincludes more than one processing device, the processing devices may becentrally located (e.g., directly coupled together via a wired and/orwireless bus structure) or may be distributedly located (e.g., cloudcomputing via indirect coupling via a local area network and/or a widearea network).

Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

Each of the interfaces 62, 66, 68, 70, and 72 includes a combination ofhardware (e.g., connectors, wiring, etc.) and may further includeoperational instructions stored on memory (e.g., driver software) thatare executed by one or more of the processing modules 50-1 through 50-Nand/or a processing circuit within the interface. Each of the interfacescouples to one or more components of the servers. For example, one ofthe IO device interfaces 62 couples to an audio output device 78. Asanother example, one of the memory interfaces 70 couples to flash memory92 and another one of the memory interfaces 70 couples to cloud memory98 (e.g., an on-line storage system and/or on-line backup system). Inother embodiments, the servers may include more or less devices andmodules than shown in this example embodiment of the servers.

FIG. 3 is a schematic block diagram of an embodiment of the variousdevices of the computing system 10 of FIG. 1, including the user devices12-1 through 12-N and the wireless user devices 14-1 through 14-N. Thevarious devices include the visual output device 74 of FIG. 2, the userinput device 76 of FIG. 2, the audio output device 78 of FIG. 2, thevisual input device 80 of FIG. 2, and one or more sensors 82.

The sensor may implemented internally and/or externally to the device.Example sensors includes a still camera, a video camera, servo motorsassociated with a camera, a position detector, a smoke detector, a gasdetector, a motion sensor, an accelerometer, velocity detector, acompass, a gyro, a temperature sensor, a pressure sensor, an altitudesensor, a humidity detector, a moisture detector, an imaging sensor, anda biometric sensor. Further examples of the sensor include an infraredsensor, an audio sensor, an ultrasonic sensor, a proximity detector, amagnetic field detector, a biomaterial detector, a radiation detector, aweight detector, a density detector, a chemical analysis detector, afluid flow volume sensor, a DNA reader, a wind speed sensor, a winddirection sensor, and an object detection sensor.

Further examples of the sensor include an object identifier sensor, amotion recognition detector, a battery level detector, a roomtemperature sensor, a sound detector, a smoke detector, an intrusiondetector, a motion detector, a door position sensor, a window positionsensor, and a sunlight detector. Still further sensor examples includemedical category sensors including: a pulse rate monitor, a heart rhythmmonitor, a breathing detector, a blood pressure monitor, a blood glucoselevel detector, blood type, an electrocardiogram sensor, a body massdetector, an imaging sensor, a microphone, body temperature, etc.

The various devices further include the computing core 52 of FIG. 2, theone or more universal serial bus (USB) devices (USB devices 1-U) of FIG.2, the one or more peripheral devices (e.g., peripheral devices 1-P) ofFIG. 2, and the one or more memories of FIG. 2 (e.g., flash memories 92,HD memories 94, SS memories 96, and/or cloud memories 98). The variousdevices further include the one or more wireless location modems 84 ofFIG. 2, the one or more wireless communication modems 86-1 through 86-Nof FIG. 2, the telco interface 102 of FIG. 2, the wired local areanetwork (LAN) 88 of FIG. 2, and the wired wide area network (WAN) 90 ofFIG. 2. In other embodiments, the various devices may include more orless internal devices and modules than shown in this example embodimentof the various devices.

FIGS. 4A and 4B are schematic block diagrams of another embodiment of acomputing system that includes one or more of the user device 12-1 ofFIG. 1, the wireless user device 14-1 of FIG. 1, the content source 16-1of FIG. 1, the transactional server 18-1 of FIG. 1, the user device 12-2of FIG. 1, and the AI server 20-1 of FIG. 1. The AI server 20-1 includesthe processing module 50-1 (e.g., associated with the servers) of FIG.2, where the processing module 50-1 includes a collections module 120,an identigen entigen intelligence (IEI) module 122, and a query module124. Alternatively, the collections module 120, the IEI module 122, andthe query module 124 may be implemented by the processing module 50-1(e.g., associated with the various user devices) of FIG. 3. Thecomputing system functions to interpret content to produce a response toa query.

FIG. 4A illustrates an example of the interpreting of the content toproduce the response to the query where the collections module 120interprets (e.g., based on an interpretation approach such as rules) atleast one of a collections request 132 from the query module 124 and acollections request within collections information 130 from the IEImodule 122 to produce content request information (e.g., potentialsources, content descriptors of desired content). Alternatively, or inaddition to, the collections module 120 may facilitate gathering furthercontent based on a plurality of collection requests from a plurality ofdevices of the computing system 10 of FIG. 1.

The collections request 132 is utilized to facilitate collection ofcontent, where the content may be received in a real-time fashion onceor at desired intervals, or in a static fashion from previous discretetime frames. For instance, the query module 124 issues the collectionsrequest 132 to facilitate collection of content as a background activityto support a long-term query (e.g., how many domestic airline flightsover the next seven days include travelers between the age of 18 and 35years old). The collections request 132 may include one or more of arequester identifier (ID), a content type (e.g., language, dialect,media type, topic, etc.), a content source indicator, securitycredentials (e.g., an authorization level, a password, a user ID,parameters utilized for encryption, etc.), a desired content qualitylevel, trigger information (e.g., parameters under which to collectcontent based on a pre-event, an event (i.e., content quality levelreaches a threshold to cause the trigger, trueness), or a timeframe), adesired format, and a desired timing associated with the content.

Having interpreted the collections request 132, the collections module120 selects a source of content based on the content requestinformation. The selecting includes one or more of identifying one ormore potential sources based on the content request information,selecting the source of content from the potential sources utilizing aselection approach (e.g., favorable history, a favorable security level,favorable accessibility, favorable cost, favorable performance, etc.).For example, the collections module 120 selects the content source 16-1when the content source 16-1 is known to provide a favorable contentquality level for a domain associated with the collections request 132.

Having selected the source of content, the collections module 120 issuesa content request 126 to the selected source of content. The issuingincludes generating the content request 126 based on the content requestinformation for the selected source of content and sending the contentrequest 126 to the selected source of content. The content request 126may include one or more of a content type indicator, a requester ID,security credentials for content access, and any other informationassociated with the collections request 132. For example, thecollections module 120 sends the content request 126, via the corenetwork 24 of FIG. 1, to the content source 16-1. Alternatively, or inaddition to, the collections module 120 may send a similar contentrequest 126 to one or more of the user device 12-1, the wireless userdevice 14-1, and the transactional server 18-1 to facilitate collectingof further content.

In response to the content request 126, the collections module 120receives one or more content responses 128. The content response 128includes one or more of content associated with the content source, acontent source identifier, security credential processing information,and any other information pertaining to the desired content. Havingreceived the content response 128, the collections module 120 interpretsthe received content response 128 to produce collections information130, where the collections information 130 further includes acollections response from the collections module 120 to the IEI module122.

The collections response includes one or more of transformed content(e.g., completed sentences and paragraphs), timing informationassociated with the content, a content source ID, and a content qualitylevel. Having generated the collections response of the collectionsinformation 130, the collections module 120 sends the collectionsinformation 130 to the IEI module 122. Having received the collectionsinformation 130 from the collections module 120, the IEI module 122interprets the further content of the content response to generatefurther knowledge, where the further knowledge is stored in a memoryassociated with the IEI module 122 to facilitate subsequent answering ofquestions posed in received queries.

FIG. 4B further illustrates the example of the interpreting of thecontent to produce the response to the query where, the query module 124interprets a received query request 136 from a requester to produce aninterpretation of the query request. For example, the query module 124receives the query request 136 from the user device 12-2, and/or fromone or more of the wireless user device 14-2 and the transactionalserver 18-2. The query request 136 includes one or more of an identifier(ID) associated with the request (e.g., requester ID, ID of an entity tosend a response to), a question, question constraints (e.g., within atimeframe, within a geographic area, within a domain of knowledge,etc.), and content associated with the question (e.g., which may beanalyzed for new knowledge itself).

The interpreting of the query request 136 includes determining whetherto issue a request to the IEI module 122 (e.g., a question, perhaps withcontent) and/or to issue a request to the collections module 120 (e.g.,for further background content). For example, the query module 124produces the interpretation of the query request to indicate to send therequest directly to the IEI module 122 when the question is associatedwith a simple non-time varying function answer (e.g., question: “howmany hydrogen atoms does a molecule of water have?”).

Having interpreted the query request 136, the query module 124 issues atleast one of an IEI request as query information 138 to the IEI module122 (e.g., when receiving a simple new query request) and a collectionsrequest 132 to the collections module 120 (e.g., based on two or morequery requests 136 requiring more substantive content gathering). TheIEI request of the query information 138 includes one or more of anidentifier (ID) of the query module 124, an ID of the requester (e.g.,the user device 12-2), a question (e.g., with regards to content foranalysis, with regards to knowledge minded by the AI server from generalcontent), one or more constraints (e.g., assumptions, restrictions,etc.) associated with the question, content for analysis of thequestion, and timing information (e.g., a date range for relevance ofthe question).

Having received the query information 138 that includes the IEI requestfrom the query module 124, the IEI module 122 determines whether asatisfactory response can be generated based on currently availableknowledge, including that of the query request 136. The determiningincludes indicating that the satisfactory response cannot be generatedwhen an estimated quality level of an answer falls below a minimumquality threshold level. When the satisfactory response cannot begenerated, the IEI module 122 facilitates collecting more content. Thefacilitating includes issuing a collections request to the collectionsmodule 120 of the AI server 20-1 and/or to another server or userdevice, and interpreting a subsequent collections response 134 ofcollections information 130 that includes further content to producefurther knowledge to enable a more favorable answer.

When the IEI module 122 indicates that the satisfactory response can begenerated, the IEI module 122 issues an IEI response as queryinformation 138 to the query module 124. The IEI response includes oneor more of one or more answers, timing relevance of the one or moreanswers, an estimated quality level of each answer, and one or moreassumptions associated with the answer. The issuing includes generatingthe IEI response based on the collections response 134 of thecollections information 130 and the IEI request, and sending the IEIresponse as the query information 138 to the query module 124.Alternatively, or in addition to, at least some of the further contentcollected by the collections module 120 is utilized to generate acollections response 134 issued by the collections module 120 to thequery module 124. The collections response 134 includes one or more offurther content, a content availability indicator (e.g., when, where,required credentials, etc.), a content freshness indicator (e.g.,timestamps, predicted time availability), content source identifiers,and a content quality level.

Having received the query information 138 from the IEI module 122, thequery module 124 issues a query response 140 to the requester based onthe IEI response and/or the collections response 134 directly from thecollections module 120, where the collection module 120 generates thecollections response 134 based on collected content and the collectionsrequest 132. The query response 140 includes one or more of an answer,answer timing, an answer quality level, and answer assumptions.

FIG. 4C is a logic diagram of an embodiment of a method for interpretingcontent to produce a response to a query within a computing system. Inparticular, a method is presented for use in conjunction with one ormore functions and features described in conjunction with FIGS. 1-3,4A-4B, and also FIG. 4C. The method includes step 150 where acollections module of a processing module of one or more computingdevices (e.g., of one or more servers) interprets a collections requestto produce content request information. The interpreting may include oneor more of identifying a desired content source, identifying a contenttype, identifying a content domain, and identifying content timingrequirements.

The method continues at step 152 where the collections module selects asource of content based on the content request information. For example,the collections module identifies one or more potential sources based onthe content request information and selects the source of content fromthe potential sources utilizing a selection approach (e.g., based on oneor more of favorable history, a favorable security level, favorableaccessibility, favorable cost, favorable performance, etc.). The methodcontinues at step 154 where the collections module issues a contentrequest to the selected source of content. The issuing includesgenerating a content request based on the content request informationfor the selected source of content and sending the content request tothe selected source of content.

The method continues at step 156 where the collections module issuescollections information to an identigen entigen intelligence (IEI)module based on a received content response, where the IEI moduleextracts further knowledge from newly obtained content from the one ormore received content responses. For example, the collections modulegenerates the collections information based on newly obtained contentfrom the one or more received content responses of the selected sourceof content.

The method continues at step 158 where a query module interprets areceived query request from a requester to produce an interpretation ofthe query request. The interpreting may include determining whether toissue a request to the IEI module (e.g., a question) or to issue arequest to the collections module to gather further background content.The method continues at step 160 where the query module issues a furthercollections request. For example, when receiving a new query request,the query module generates a request for the IEI module. As anotherexample, when receiving a plurality of query requests for similarquestions, the query module generates a request for the collectionsmodule to gather further background content.

The method continues at step 162 where the IEI module determines whethera satisfactory query response can be generated when receiving therequest from the query module. For example, the IEI module indicatesthat the satisfactory query response cannot be generated when anestimated quality level of an answer is below a minimum answer qualitythreshold level. The method branches to step 166 when the IEI moduledetermines that the satisfactory query response can be generated. Themethod continues to step 164 when the IEI module determines that thesatisfactory query response cannot be generated. When the satisfactoryquery response cannot be generated, the method continues at step 164where the IEI module facilitates collecting more content. The methodloops back to step 150.

When the satisfactory query response can be generated, the methodcontinues at step 166 where the IEI module issues an IEI response to thequery module. The issuing includes generating the IEI response based onthe collections response and the IEI request, and sending the IEIresponse to the query module. The method continues at step 168 where thequery module issues a query response to the requester. For example, thequery module generates the query response based on the IEI responseand/or a collections response from the collections module and sends thequery response to the requester, where the collections module generatesthe collections response based on collected content and the collectionsrequest.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 5A is a schematic block diagram of an embodiment of the collectionsmodule 120 of FIG. 4A that includes a content acquisition module 180, acontent selection module 182, a source selection module 184, a contentsecurity module 186, an acquisition timing module 188, a contenttransformation module 190, and a content quality module 192. Generally,an embodiment of this invention presents solutions where the collectionsmodule 120 supports collecting content. In an example of operation ofthe collecting of the content, the content acquisition module 180receives a collections request 132 from a requester. The contentacquisition module 180 obtains content selection information 194 basedon the collections request 132. The content selection information 194includes one or more of content requirements, a desired content typeindicator, a desired content source identifier, a content typeindicator, a candidate source identifier (ID), and a content profile(e.g., a template of typical parameters of the content). For example,the content acquisition module 180 receives the content selectioninformation 194 from the content selection module 182, where the contentselection module 182 generates the content selection information 194based on a content selection information request from the contentacquisition module 180 and where the content acquisition module 180generates the content selection information request based on thecollections request 132.

The content acquisition module 180 obtains source selection information196 based on the collections request 132. The source selectioninformation 196 includes one or more of candidate source identifiers, acontent profile, selected sources, source priority levels, andrecommended source access timing. For example, the content acquisitionmodule 180 receives the source selection information 196 from the sourceselection module 184, where the source selection module 184 generatesthe source selection information 196 based on a source selectioninformation request from the content acquisition module 180 and wherethe content acquisition module 180 generates the source selectioninformation request based on the collections request 132.

The content acquisition module 180 obtains acquisition timinginformation 200 based on the collections request 132. The acquisitiontiming information 200 includes one or more of recommended source accesstiming, confirmed source access timing, source access testing results,estimated velocity of content update's, content precious, timestamps,predicted time availability, required content acquisition triggers,content acquisition trigger detection indicators, and a duplicativeindicator with a pending content request. For example, the contentacquisition module 180 receives the acquisition timing information 200from the acquisition timing module 188, where the acquisition timingmodule 188 generates the acquisition timing information 200 based on anacquisition timing information request from the content acquisitionmodule 180 and where the content acquisition module 180 generates theacquisition timing information request based on the collections request132.

Having obtained the content selection information 194, the sourceselection information 196, and the acquisition timing information 200,the content acquisition module 180 issues a content request 126 to acontent source utilizing security information 198 from the contentsecurity module 186, where the content acquisition module 180 generatesthe content request 126 in accordance with the content selectioninformation 194, the source selection information 196, and theacquisition timing information 200. The security information 198includes one or more of source priority requirements, requester securityinformation, available security procedures, and security credentials fortrust and/or encryption. For example, the content acquisition module 180generates the content request 126 to request a particular content typein accordance with the content selection information 194 and to includesecurity parameters of the security information 198, initiates sendingof the content request 126 in accordance with the acquisition timinginformation 200, and sends the content request 126 to a particulartargeted content source in accordance with the source selectioninformation 196.

In response to receiving a content response 128, the content acquisitionmodule 180 determines the quality level of received content extractedfrom the content response 128. For example, the content acquisitionmodule 180 receives content quality information 204 from the contentquality module 192, where the content quality module 192 generates thequality level of the received content based on receiving a contentquality request from the content acquisition module 180 and where thecontent acquisition module 180 generates the content quality requestbased on content extracted from the content response 128. The contentquality information includes one or more of a content reliabilitythreshold range, a content accuracy threshold range, a desired contentquality level, a predicted content quality level, and a predicted levelof trust. When the quality level is below a minimum desired qualitythreshold level, the content acquisition module 180 facilitatesacquisition of further content. The facilitating includes issuinganother content request 126 to a same content source and/or to anothercontent source to receive and interpret further received content. Whenthe quality level is above the minimum desired quality threshold level,the content acquisition module 180 issues a collections response 134 tothe requester. The issuing includes processing the content in accordancewith a transformation approach to produce transformed content,generating the collections response 134 to include the transformedcontent, and sending the collections response 134 to the requester. Theprocessing of the content to produce the transformed content includesreceiving content transformation information 202 from the contenttransformation module 190, where the content transformation module 190transforms the content in accordance with the transformation approach toproduce the transformed content. The content transformation informationincludes a desired format, available formats, recommended formatting,the received content, transformation instructions, and the transformedcontent.

FIG. 5B is a logic diagram of an embodiment of a method for obtainingcontent within a computing system. In particular, a method is presentedfor use in conjunction with one or more functions and features describedin conjunction with FIGS. 1-3, 4A-4C, 5A, and also FIG. 5B. The methodincludes step 210 where a processing module of one or more processingmodules of one or more computing devices of the computing systemreceives a collections request from the requester. The method continuesat step 212 where the processing module determines content selectioninformation. The determining includes interpreting the collectionsrequest to identify requirements of the content.

The method continues at step 214 where the processing module determinessource selection information. The determining includes interpreting thecollections request to identify and select one or more sources for thecontent to be collected. The method continues at step 216 where theprocessing module determines acquisition timing information. Thedetermining includes interpreting the collections request to identifytiming requirements for the acquisition of the content from the one ormore sources. The method continues at step 218 where the processingmodule issues a content request utilizing security information and inaccordance with one or more of the content selection information, thesource selection information, and the acquisition timing information.For example, the processing module issues the content request to the oneor more sources for the content in accordance with the contentrequirements, where the sending of the request is in accordance with theacquisition timing information.

The method continues at step 220 where the processing module determinesa content quality level for received content area the determiningincludes receiving the content from the one or more sources, obtainingcontent quality information for the received content based on a qualityanalysis of the received content. The method branches to step 224 whenthe content quality level is favorable and the method continues to step222 when the quality level is unfavorable. For example, the processingmodule determines that the content quality level is favorable when thecontent quality level is equal to or above a minimum quality thresholdlevel and determines that the content quality level is unfavorable whenthe content quality level is less than the minimum quality thresholdlevel.

When the content quality level is unfavorable, the method continues atstep 222 where the processing module facilitates acquisition and furthercontent. For example, the processing module issues further contentrequests and receives further content for analysis. When the contentquality level is favorable, the method continues at step 224 where theprocessing module issues a collections response to the requester. Theissuing includes generating the collections response and sending thecollections response to the requester. The generating of the collectionsresponse may include transforming the received content into transformedcontent in accordance with a transformation approach (e.g.,reformatting, interpreting absolute meaning and translating into anotherlanguage in accordance with the absolute meaning, etc.).

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 5C is a schematic block diagram of an embodiment of the querymodule 124 of FIG. 4A that includes an answer acquisition module 230, acontent requirements module 232 a source requirements module 234, acontent security module 236, an answer timing module 238, an answertransformation module 240, and an answer quality module 242. Generally,an embodiment of this invention presents solutions where the querymodule 124 supports responding to a query.

In an example of operation of the responding to the query, the answeracquisition module 230 receives a query request 136 from a requester.The answer acquisition module 230 obtains content requirementsinformation 248 based on the query request 136. The content requirementsinformation 248 includes one or more of content parameters, a desiredcontent type, a desired content source if any, a content type if any,candidate source identifiers, a content profile, and a question of thequery request 136. For example, the answer acquisition module 230receives the content requirements information 248 from the contentrequirements module 232, where the content requirements module 232generates the content requirements information 248 based on a contentrequirements information request from the answer acquisition module 230and where the answer acquisition module 230 generates the contentrequirements information request based on the query request 136.

The answer acquisition module 230 obtains source requirementsinformation 250 based on the query request 136. The source requirementsinformation 250 includes one or more of candidate source identifiers, acontent profile, a desired source parameter, recommended sourceparameters, source priority levels, and recommended source accesstiming. For example, the answer acquisition module 230 receives thesource requirements information 250 from the source requirements module234, where the source requirements module 234 generates the sourcerequirements information 250 based on a source requirements informationrequest from the answer acquisition module 230 and where the answeracquisition module 230 generates the source requirements informationrequest based on the query request 136.

The answer acquisition module 230 obtains answer timing information 254based on the query request 136. The answer timing information 254includes one or more of requested answer timing, confirmed answertiming, source access testing results, estimated velocity of contentupdates, content freshness, timestamps, predicted time available,requested content acquisition trigger, and a content acquisition triggerdetected indicator. For example, the answer acquisition module 230receives the answer timing information 254 from the answer timing module238, where the answer timing module 238 generates the answer timinginformation 254 based on an answer timing information request from theanswer acquisition module 230 and where the answer acquisition module230 generates the answer timing information request based on the queryrequest 136.

Having obtained the content requirements information 248, the sourcerequirements information 250, and the answer timing information 254, theanswer acquisition module 230 determines whether to issue an IEI request244 and/or a collections request 132 based on one or more of the contentrequirements information 248, the source requirements information 250,and the answer timing information 254. For example, the answeracquisition module 230 selects the IEI request 244 when an immediateanswer to a simple query request 136 is required and is expected to havea favorable quality level. As another example, the answer acquisitionmodule 230 selects the collections request 132 when a longer-term answeris required as indicated by the answer timing information to beforeand/or when the query request 136 has an unfavorable quality level.

When issuing the LEI request 244, the answer acquisition module 230generates the IEI request 244 in accordance with security information252 received from the content security module 236 and based on one ormore of the content requirements information 248, the sourcerequirements information 250, and the answer timing information 254.Having generated the IEI request 244, the answer acquisition module 230sends the IEI request 244 to at least one IEI module.

When issuing the collections request 132, the answer acquisition module230 generates the collections request 132 in accordance with thesecurity information 252 received from the content security module 236and based on one or more of the content requirements information 248,the source requirements information 250, and the answer timinginformation 254. Having generated the collections request 132, theanswer acquisition module 230 sends the collections request 132 to atleast one collections module. Alternatively, the answer acquisitionmodule 230 facilitate sending of the collections request 132 to one ormore various user devices (e.g., to access a subject matter expert).

The answer acquisition module 230 determines a quality level of areceived answer extracted from a collections response 134 and/or an IEIresponse 246. For example, the answer acquisition module 230 extractsthe quality level of the received answer from answer quality information258 received from the answer quality module 242 in response to an answerquality request from the answer acquisition module 230. When the qualitylevel is unfavorable, the answer acquisition module 230 facilitatesobtaining a further answer. The facilitation includes issuing at leastone of a further IEI request 244 and a further collections request 132to generate a further answer for further quality testing. When thequality level is favorable, the answer acquisition module 230 issues aquery response 140 to the requester. The issuing includes generating thequery response 140 based on answer transformation information 256received from the answer transformation module 240, where the answertransformation module 240 generates the answer transformationinformation 256 to include a transformed answer based on receiving theanswer from the answer acquisition module 230. The answer transformationinformation 250 6A further include the question, a desired format of theanswer, available formats, recommended formatting, received IEIresponses, transformation instructions, and transformed IEI responsesinto an answer.

FIG. 5D is a logic diagram of an embodiment of a method for providing aresponse to a query within a computing system. In particular, a methodis presented for use in conjunction with one or more functions andfeatures described in conjunction with FIGS. 1-3, 4A-4C, 5C, and alsoFIG. 5D. The method includes step 270 where a processing module of oneor more processing modules of one or more computing devices of thecomputing system receives a query request (e.g., a question) from arequester. The method continues at step 272 where the processing moduledetermines content requirements information. The determining includesinterpreting the query request to produce the content requirements. Themethod continues at step 274 where the processing module determinessource requirements information. The determining includes interpretingthe query request to produce the source requirements. The methodcontinues at step 276 where the processing module determines answertiming information. The determining includes interpreting the queryrequest to produce the answer timing information.

The method continues at step 278 the processing module determineswhether to issue an IEI request and/or a collections request. Forexample, the determining includes selecting the IEI request when theanswer timing information indicates that a simple one-time answer isappropriate. As another example, the processing module selects thecollections request when the answer timing information indicates thatthe answer is associated with a series of events over an event timeframe.

When issuing the IEI request, the method continues at step 280 where theprocessing module issues the LEI request to an LEI module. The issuingincludes generating the IEI request in accordance with securityinformation and based on one or more of the content requirementsinformation, the source requirements information, and the answer timinginformation.

When issuing the collections request, the method continues at step 282where the processing module issues the collections request to acollections module. The issuing includes generating the collectionsrequest in accordance with the security information and based on one ormore of the content requirements information, the source requirementsinformation, and the answer timing information. Alternatively, theprocessing module issues both the LEI request and the collectionsrequest when a satisfactory partial answer may be provided based on acorresponding LEI response and a further more generalized and specificanswer may be provided based on a corresponding collections response andassociated further LEI response.

The method continues at step 284 where the processing module determinesa quality level of a received answer. The determining includesextracting the answer from the collections response and/or the LEIresponse and interpreting the answer in accordance with one or more ofthe content requirements information, the source requirementsinformation, the answer timing information, and the query request toproduce the quality level. The method branches to step 288 when thequality level is favorable and the method continues to step 286 when thequality level is unfavorable. For example, the processing moduleindicates that the quality level is favorable when the quality level isequal to or greater than a minimum answer quality threshold level. Asanother example, the processing module indicates that the quality levelis unfavorable when the quality level is less than the minimum answerquality threshold level.

When the quality level is unfavorable, the method continues at step 286where the processing module obtains a further answer. The obtainingincludes at least one of issuing a further LEI request and a furthercollections request to facilitate obtaining of a further answer forfurther answer quality level testing as the method loops back to step270. When the quality level is favorable, the method continues at step288 where the processing module issues a query response to therequester. The issuing includes transforming the answer into atransformed answer in accordance with an answer transformation approach(e.g., formatting, further interpretations of the virtual question inlight of the answer and further knowledge) and sending the transformedanswer to the requester as the query response.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 5E is a schematic block diagram of an embodiment of the identigenentigen intelligence (IEI) module 122 of FIG. 4A that includes a contentingestion module 300, an element identification module 302, andinterpretation module 304, and answer resolution module 306, and an IEIcontrol module 308. Generally, an embodiment of this invention presentssolutions where the IEI module 122 supports interpreting content toproduce knowledge that may be utilized to answer questions.

In an example of operation of the producing and utilizing of theknowledge, the content ingestion module 300 generates formatted content314 based on question content 312 and/or source content 310, where theIEI module 122 receives an IEI request 244 that includes the questioncontent 312 and the IEI module 122 receives a collections response 134that includes the source content 310. The source content 310 includescontent from a source extracted from the collections response 134. Thequestion content 312 includes content extracted from the IEI request 244(e.g., content paired with a question). The content ingestion module 300generates the formatted content 314 in accordance with a formattingapproach (e.g., creating proper sentences from words of the content).The formatted content 314 includes modified content that is compatiblewith subsequent element identification (e.g., complete sentences,combinations of words and interpreted sounds and/or inflection cues withtemporal associations of words).

The element identification module 302 processes the formatted content314 based on element rules 318 and an element list 332 to produceidentified element information 340. Rules 316 includes the element rules318 (e.g., match, partial match, language translation, etc.). Lists 330includes the element list 332 (e.g., element ID, element context ID,element usage ID, words, characters, symbols etc.). The IEI controlmodule 308 may provide the rules 316 and the lists 330 by accessingstored data 360 from a memory associated with the IEI module 122.Generally, an embodiment of this invention presents solutions where thestored data 360 may further include one or more of a descriptivedictionary, categories, representations of element sets, element list,sequence data, pending questions, pending request, recognized elements,unrecognized elements, errors, etc.

The identified element information 340 includes one or more ofidentifiers of elements identified in the formatted content 314, mayinclude ordering and/or sequencing and grouping information. Forexample, the element identification module 302 compares elements of theformatted content 314 to known elements of the element list 332 toproduce identifiers of the known elements as the identified elementinformation 340 in accordance with the element rules 318. Alternatively,the element identification module 302 outputs un-identified elementinformation 342 to the IEI control module 308, where the un-identifiedelement information 342 includes temporary identifiers for elements notidentifiable from the formatted content 314 when compared to the elementlist 332.

The interpretation module 304 processes the identified elementinformation 340 in accordance with interpretation rules 320 (e.g.,potentially valid permutations of various combinations of identifiedelements), question information 346 (e.g., a question extracted from theIEI request 244 which may be paired with content associated with thequestion), and a groupings list 334 (e.g., representations of associatedgroups of representations of things, a set of element identifiers, validelement usage IDs in accordance with similar, an element context,permutations of sets of identifiers for possible interpretations of asentence or other) to produce interpreted information 344. Theinterpreted information 344 includes potentially valid interpretationsof combinations of identified elements. Generally, an embodiment of thisinvention presents solutions where the interpretation module 304supports producing the interpreted information 344 by consideringpermutations of the identified element information 340 in accordancewith the interpretation rules 320 and the groupings list 334.

The answer resolution module 306 processes the interpreted information344 based on answer rules 322 (e.g., guidance to extract a desiredanswer), the question information 346, and inferred question information352 (e.g., posed by the IEI control module or analysis of generalcollections of content or refinement of a stated question from arequest) to produce preliminary answers 354 and an answer quality level356. The answer generally lies in the interpreted information 344 asboth new content received and knowledge based on groupings list 334generated based on previously received content. The preliminary answers354 includes an answer to a stated or inferred question that subjectfurther refinement. The answer quality level 356 includes adetermination of a quality level of the preliminary answers 354 based onthe answer rules 322. The inferred question information 352 may furtherbe associated with time information 348, where the time informationincludes one or more of current real-time, a time reference associatedwith entity submitting a request, and a time reference of a collectionsresponse.

When the IEI control module 308 determines that the answer quality level356 is below an answer quality threshold level, the IEI control module308 facilitates collecting of further content (e.g., by issuing acollections request 132 and receiving corresponding collectionsresponses 134 for analysis). When the answer quality level 356 comparesfavorably to the answer quality threshold level, the IEI control module308 issues an IEI response 246 based on the preliminary answers 354.When receiving training information 358, the IEI control module 308facilitates updating of one or more of the lists 330 and the rules 316and stores the updated list 330 and the updated rules 316 in thememories as updated stored data 360.

FIG. 5F is a logic diagram of an embodiment of a method for analyzingcontent within a computing system. In particular, a method is presentedfor use in conjunction with one or more functions and features describedin conjunction with FIGS. 1-3, 4A-4C, 5E, and also FIG. 5F. The methodincludes step 370 where a processing module of one or more processingmodules of one or more computing devices of the computing systemfacilitates updating of one or more rules and lists based on one or moreof received training information and received content. For example, theprocessing module updates rules with received rules to produce updatedrules and updates element lists with received elements to produceupdated element lists. As another example, the processing moduleinterprets the received content to identify a new word for at leasttemporary inclusion in the updated element list.

The method continues at step 372 where the processing module transformsat least some of the received content into formatted content. Forexample, the processing module processes the received content inaccordance with a transformation approach to produce the formattedcontent, where the formatted content supports compatibility withsubsequent element identification (e.g., typical sentence structures ofgroups of words).

The method continues at step 374 where the processing module processesthe formatted content based on the rules and the lists to produceidentified element information and/or an identified element information.For example, the processing module compares the formatted content toelement lists to identify a match producing identifiers for identifiedelements or new identifiers for unidentified elements when there is nomatch.

The method continues at step 376 with a processing module processes theidentified element information based on rules, the lists, and questioninformation to produce interpreted information. For example, theprocessing module compares the identified element information toassociated groups of representations of things to generate potentiallyvalid interpretations of combinations of identified elements.

The method continues at step 378 where the processing module processesthe interpreted information based on the rules, the questioninformation, and inferred question information to produce preliminaryanswers. For example, the processing module matches the interpretedinformation to one or more answers (e.g., embedded knowledge based on afact base built from previously received content) with highestcorrectness likelihood levels that is subject to further refinement.

The method continues at step 380 where the processing module generatesan answer quality level based on the preliminary answers, the rules, andthe inferred question information. For example, the processing modulepredicts the answer correctness likelihood level based on the rules, theinferred question information, and the question information. The methodbranches to step 384 when the answer quality level is favorable and themethod continues to step 382 when the answer quality level isunfavorable. For example, the generating of the answer quality levelfurther includes the processing module indicating that the answerquality level is favorable when the answer quality level is greater thanor equal to a minimum answer quality threshold level. As anotherexample, the generating of the answer quality level further includes theprocessing module indicating that the answer quality level isunfavorable when the answer quality level is less than the minimumanswer quality threshold level.

When the answer quality level is unfavorable, the method continues atstep 382 where the processing module facilitates gathering clarifyinginformation. For example, the processing module issues a collectionsrequest to facilitate receiving further content and or request questionclarification from a question requester. When the answer quality levelis favorable, the method continues at step 384 where the processingmodule issues a response that includes one or more answers based on thepreliminary answers and/or further updated preliminary answers based ongathering further content. For example, the processing module generatesa response that includes one or more answers and the answer qualitylevel and issues the response to the requester.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 6A is a schematic block diagram of an embodiment of the elementidentification module 302 of FIG. 5A and the interpretation module 304of FIG. 5A. The element identification module 302 includes an elementmatching module 400 and an element grouping module 402. Theinterpretation module 304 includes a grouping matching module 404 and agrouping interpretation module 406. Generally, an embodiment of thisinvention presents solutions where the element identification module 302supports identifying potentially valid permutations of groupings ofelements while the interpretation module 304 interprets the potentiallyvalid permutations of groupings of elements to produce interpretedinformation that includes the most likely of groupings based on aquestion.

In an example of operation of the identifying of the potentially validpermutations of groupings of elements, when matching elements of theformatted content 314, the element matching module 400 generates matchedelements 412 (e.g., identifiers of elements contained in the formattedcontent 314) based on the element list 332. For example, the elementmatching module 400 matches a received element to an element of theelement list 332 and outputs the matched elements 412 to include anidentifier of the matched element. When finding elements that areunidentified, the element matching module 400 outputs un-recognizedwords information 408 (e.g., words not in the element list 332, maytemporarily add) as part of un-identified element information 342. Forexample, the element matching module 400 indicates that a match cannotbe made between a received element of the formatted content 314,generates the unrecognized words info 408 to include the receivedelement and/or a temporary identifier, and issues and updated elementlist 414 that includes the temporary identifier and the correspondingunidentified received element.

The element grouping module 402 analyzes the matched elements 412 inaccordance with element rules 318 to produce grouping error information410 (e.g., incorrect sentence structure indicators) when a structuralerror is detected. The element grouping module 402 produces identifiedelement information 340 when favorable structure is associated with thematched elements in accordance with the element rules 318. Theidentified element information 340 may further include groupinginformation of the plurality of permutations of groups of elements(e.g., several possible interpretations), where the grouping informationincludes one or more groups of words forming an associated set and/orsuper-group set of two or more subsets when subsets share a common coreelement.

In an example of operation of the interpreting of the potentially validpermutations of groupings of elements to produce the interpretedinformation, the grouping matching module 404 analyzes the identifiedelement information 340 in accordance with a groupings list 334 toproduce validated groupings information 416. For example, the groupingmatching module 404 compares a grouping aspect of the identified elementinformation 340 (e.g., for each permutation of groups of elements ofpossible interpretations), generates the validated groupings information416 to include identification of valid permutations aligned with thegroupings list 334. Alternatively, or in addition to, the groupingmatching module 404 generates an updated groupings list 418 whendetermining a new valid grouping (e.g., has favorable structure andinterpreted meaning) that is to be added to the groupings list 334.

The grouping interpretation module 406 interprets the validatedgroupings information 416 based on the question information 346 and inaccordance with the interpretation rules 320 to produce interpretedinformation 344 (e.g., most likely interpretations, next most likelyinterpretations, etc.). For example, the grouping interpretation module406 obtains context, obtains favorable historical interpretations,processes the validated groupings based on interpretation rules 320,where each interpretation is associated with a correctness likelihoodlevel.

FIG. 6B is a logic diagram of an embodiment of a method for interpretinginformation within a computing system. In particular, a method ispresented for use in conjunction with one or more functions and featuresdescribed in conjunction with FIGS. 1-3, 4A-4C, 5E-5F, 6A, and also FIG.6B. The method includes step 430 where a processing module of one ormore processing modules of one or more computing devices of thecomputing system analyzes formatted content. For example, the processingmodule attempt to match a received element of the formatted content toone or more elements of an elements list. When there is no match, themethod branches to step 434 and when there is a match, the methodcontinues to step 432. When there is a match, the method continues atstep 432 where the processing module outputs matched elements (e.g., toinclude the matched element and/or an identifier of the matchedelement). When there is no match, the method continues at step 434 wherethe processing module outputs unrecognized words (e.g., elements and/ora temporary identifier for the unmatched element).

The method continues at step 436 where the processing module analyzesmatched elements. For example, the processing module attempt to match adetected structure of the matched elements (e.g., chained elements as ina received sequence) to favorable structures in accordance with elementrules. The method branches to step 440 when the analysis is unfavorableand the method continues to step 438 when the analysis is favorable.When the analysis is favorable matching a detected structure to thefavorable structure of the element rules, the method continues at step438 where the processing module outputs identified element information(e.g., an identifier of the favorable structure, identifiers of each ofthe detected elements). When the analysis is unfavorable matching adetected structure to the favorable structure of the element rules, themethod continues at step 440 where the processing module outputsgrouping error information (e.g., a representation of the incorrectstructure, identifiers of the elements of the incorrect structure, atemporary new identifier of the incorrect structure).

The method continues at step 442 where the processing module analyzesthe identified element information to produce validated groupingsinformation. For example, the processing module compares a groupingaspect of the identified element information and generates the validatedgroupings information to include identification of valid permutationsthat align with the groupings list. Alternatively, or in addition to,the processing module generates an updated groupings list whendetermining a new valid grouping.

The method continues at step 444 where the processing module interpretsthe validated groupings information to produce interpreted information.For example, the processing module obtains one or more of context andhistorical interpretations and processes the validated groupings basedon interpretation rules to generate the interpreted information, whereeach interpretation is associated with a correctness likelihood level(e.g., a quality level).

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 6C is a schematic block diagram of an embodiment of the answerresolution module 306 of FIG. 5A that includes an interim answer module460, and answer prioritization module 462, and a preliminary answerquality module 464. Generally, an embodiment of this invention presentssolutions where the answer resolution module 306 supports producing ananswer for interpreted information 344.

In an example of operation of the providing of the answer, the interimanswer module 460 analyzes the interpreted information 344 based onquestion information 346 and inferred question information 352 toproduce interim answers 466 (e.g., answers to stated and/or inferredquestions without regard to rules that is subject to furtherrefinement). The answer prioritization module 462 analyzes the interimanswers 466 based on answer rules 322 to produce preliminary answer 354.For example, the answer prioritization module 462 identifies allpossible answers from the interim answers 466 that conform to the answerrules 322.

The preliminary answer quality module 464 analyzes the preliminaryanswers 354 in accordance with the question information 346, theinferred question information 352, and the answer rules 322 to producean answer quality level 356. For example, for each of the preliminaryanswers 354, the preliminary answer quality module 464 may compare a fitof the preliminary answer 354 to a corresponding previous answer andquestion quality level, calculate the answer quality level 356 based ona level of conformance to the answer rules 322, calculate the answerquality level 356 based on alignment with the inferred questioninformation 352, and determine the answer quality level 356 based on aninterpreted correlation with the question information 346.

FIG. 6D is a logic diagram of an embodiment of a method for producing ananswer within a computing system. In particular, a method is presentedfor use in conjunction with one or more functions and features describedin conjunction with FIGS. 1-3, 4A-4C, 5E-5F, 6C, and also FIG. 6D. Themethod includes step 480 where a processing module of one or moreprocessing modules of one or more computing devices of the computingsystem analyzes received interpreted information based on questioninformation and inferred question information to produce one or moreinterim answers. For example, the processing module generates potentialanswers based on patterns consistent with previously produced knowledgeand likelihood of correctness.

The method continues at step 482 where the processing module analyzesthe one or more interim answers based on answer rules to producepreliminary answers. For example, the processing module identifies allpossible answers from the interim answers that conform to the answerrules. The method continues at step 484 where the processing moduleanalyzes the preliminary answers in accordance with the questioninformation, the inferred question information, and the answer rules toproduce an answer quality level. For example, for each of the elementaryanswers, the processing module may compare a fit of the preliminaryanswer to a corresponding previous answer-and-answer quality level,calculate the answer quality level based on performance to the answerrules, calculate answer quality level based on alignment with theinferred question information, and determine the answer quality levelbased on interpreted correlation with the question information.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 7A is an information flow diagram for interpreting informationwithin a computing system, where sets of entigens 504 are interpretedfrom sets of identigens 502 which are interpreted from sentences ofwords 500. Such identigen entigen intelligence (WI) processing of thewords (e.g., to IEI process) includes producing one or more of interimknowledge, a preliminary answer, and an answer quality level. Forexample, the IEI processing includes identifying permutations ofidentigens of a phrase of a sentence (e.g., interpreting humanexpressions to produce identigen groupings for each word of ingestedcontent), reducing the permutations of identigens (e.g., utilizing rulesto eliminate unfavorable permutations), mapping the reduced permutationsof identigens to at least one set of entigens (e.g., most likelyidentigens become the entigens) to produce the interim knowledge,processing the knowledge in accordance with a knowledge database (e.g.,comparing the set of entigens to the knowledge database) to produce apreliminary answer, and generating the answer quality level based on thepreliminary answer for a corresponding domain.

Human expressions are utilized to portray facts and fiction about thereal world. The real-world includes items, actions, and attributes. Thehuman expressions include textual words, textual symbols, images, andother sensorial information (e.g., sounds). It is known that many words,within a given language, can mean different things based on groupingsand orderings of the words. For example, the sentences of words 500 caninclude many different forms of sentences that mean vastly differentthings even when the words are very similar.

The present invention presents solutions where the computing system 10supports producing a computer-based representation of a truest meaningpossible of the human expressions given the way that multitudes of humanexpressions relate to these meanings. As a first step of the flowdiagram to transition from human representations of things to a mostprecise computer representation of the things, the computer identifiesthe words, phrases, sentences, etc. from the human expressions toproduce the sets of identigens 502. Each identigen includes anidentifier of their meaning and an identifier of an instance for eachpossible language, culture, etc. For example, the words car andautomobile share a common meaning identifier but have different instanceidentifiers since they are different words and are spelled differently.As another example, the word duck is associated both with a bird and anaction to elude even though they are spelled the same. In this examplethe bird duck has a different meaning than the elude duck and as sucheach has a different meaning identifier of the corresponding identigens.

As a second step of the flow diagram to transition from humanrepresentations of things to the most precise computer representation ofthe things, the computer extracts meaning from groupings of theidentified words, phrases, sentences, etc. to produce the sets ofentigens 504. Each entigen includes an identifier of a singleconceivable and perceivable thing in space and time (e.g., independentof language and other aspects of the human expressions). For example,the words car and automobile are different instances of the same meaningand point to a common shared entigen. As another example, the word duckfor the bird meaning has an associated unique entigen that is differentthan the entigen for the word duck for the elude meaning.

As a third step of the flow diagram to transition from human expressionsof things to the most precise computer representation of the things, thecomputer reasons facts from the extracted meanings. For example, thecomputer maintains a fact-based of the valid meanings from the validgroupings or sets of entigens so as to support subsequent inferences,deductions, rationalizations of posed questions to produce answers thatare aligned with a most factual view. As time goes on, and as an entigenhas been identified, it can encounter an experience transformations intime, space, attributes, actions, and words which are used to identifyit without creating contradictions or ever losing its identity.

FIG. 7B is a relationship block diagram illustrating an embodiment ofrelationships between things 510 and representations of things 512within a computing system. The things 510 includes conceivable andperceivable things including actions 522, items 524, and attributes 526.The representation of things 512 includes representations of things usedby humans 514 and representation of things used by of computing devices516 of embodiments of the present invention. The things 510 relates tothe representations of things used by humans 514 where the inventionpresents solutions where the computing system 10 supports mapping therepresentations of things used by humans 514 to the representations ofthings used by computing devices 516, where the representations ofthings used by computing devices 516 map back to the things 510.

The representations of things used by humans 514 includes textual words528, textual symbols 530, images (e.g., non-textual) 532, and othersensorial information 534 (e.g., sounds, sensor data, electrical fields,voice inflections, emotion representations, facial expressions,whistles, etc.). The representations of things used by computing devices516 includes identigens 518 and entigens 520. The representations ofthings used by humans 514 maps to the identigens 518 and the identigens518 map to the entigens 520. The entigens 520 uniquely maps back to thethings 510 in space and time, a truest meaning the computer is lookingfor to create knowledge and answer questions based on the knowledge.

To accommodate the mapping of the representations of things used byhumans 514 to the identigens 518, the identigens 518 is partitioned intoactenyms 544 (e.g., actions), itenyms 546 (e.g., items), attrenyms 548(e.g., attributes), and functionals 550 (e.g., that join and/ordescribe).

Each of the actenyms 544, itenyms 546, and attrenyms 548 may be furtherclassified into singulatums 552 (e.g., identify one unique entigen) andpluratums 554 (e.g., identify a plurality of entigens that havesimilarities).

Each identigen 518 is associated with an identigens identifier (IDN)536. The IDN 536 includes a meaning identifier (ID) 538 portion, aninstance ID 540 portion, and a type ID 542 portion. The meaning ID 538includes an identifier of common meaning. The instance ID 540 includesan identifier of a particular word and language. The type ID 542includes one or more identifiers for actenyms, itenyms, attrenyms,singulatums, pluratums, a time reference, and any other reference todescribe the IDN 536. The mapping of the representations of things usedby humans 514 to the identigens 518 by the computing system of thepresent invention includes determining the identigens 518 in accordancewith logic and instructions for forming groupings of words.

Generally, an embodiment of this invention presents solutions where theidentigens 518 map to the entigens 520. Multiple identigens may map to acommon unique entigen. The mapping of the identigens 518 to the entigens520 by the computing system of the present invention includesdetermining entigens in accordance with logic and instructions forforming groupings of identigens.

FIG. 7C is a diagram of an embodiment of a synonym words table 570within a computing system, where the synonym words table 570 includesmultiple fields including textual words 572, identigens 518, andentigens 520. The identigens 518 includes fields for the meaningidentifier (ID) 538 and the instance ID 540. The computing system of thepresent invention may utilize the synonym words table 570 to map textualwords 572 to identigens 518 and map the identigens 518 to entigens 520.For example, the words car, automobile, auto, bil (Swedish), carro(Spanish), and bil (Danish) all share a common meaning but are differentinstances (e.g., different words and languages). The words map to acommon meaning ID but to individual unique instant identifiers. Each ofthe different identigens map to a common entigen since they describe thesame thing.

FIG. 7D is a diagram of an embodiment of a polysemous words table 576within a computing system, where the polysemous words table 576 includesmultiple fields including textual words 572, identigens 518, andentigens 520. The identigens 518 includes fields for the meaningidentifier (ID) 538 and the instance ID 540. The computing system of thepresent invention may utilize the polysemous words table 576 to maptextual words 572 to identigens 518 and map the identigens 518 toentigens 520. For example, the word duck maps to four differentidentigens since the word duck has four associated different meanings(e.g., bird, fabric, to submerge, to elude) and instances. Each of theidentigens represent different things and hence map to four differententigens.

FIG. 7E is a diagram of an embodiment of transforming words intogroupings within a computing system that includes a words table 580, agroupings of words section to validate permutations of groupings, and agroupings table 584 to capture the valid groupings. The words table 580includes multiple fields including textual words 572, identigens 518,and entigens 520.

The identigens 518 includes fields for the meaning identifier (ID) 538,the instance ID 540, and the type ID 542. The computing system of thepresent invention may utilize the words table 580 to map textual words572 to identigens 518 and map the identigens 518 to entigens 520. Forexample, the word pilot may refer to a flyer and the action to fly. Eachmeaning has a different identigen and different entigen.

The computing system the present invention may apply rules to the fieldsof the words table 580 to validate various groupings of words. Thosethat are invalid are denoted with a “X” while those that are valid areassociated with a check mark. For example, the grouping “pilot Tom” isinvalid when the word pilot refers to flying and Tom refers to a person.The identigen combinations for the flying pilot and the person Tom aredenoted as invalid by the rules. As another example, the grouping “pilotTom” is valid when the word pilot refers to a flyer and Tom refers tothe person. The identigen combinations for the flyer pilot and theperson Tom are denoted as valid by the rules.

The groupings table 584 includes multiple fields including grouping ID586, word strings 588, identigens 518, and entigens 520. The computingsystem of the present invention may produce the groupings table 584 as astored fact base for valid and/or invalid groupings of words identifiedby their corresponding identigens. For example, the valid grouping“pilot Tom” referring to flyer Tom the person is represented with agrouping identifier of 3001 and identity and identifiers 150.001 and457.001. The entigen field 520 may indicate associated entigens thatcorrespond to the identigens. For example, entigen e717 corresponds tothe flyer pilot meaning and entigen e61 corresponds to the time theperson meaning. Alternatively, or in addition to, the entigen field 520may be populated with a single entigen identifier (ENI).

The word strings field 588 may include any number of words in a string.Different ordering of the same words can produce multiple differentstrings and even different meanings and hence entigens. More broadly,each entry (e.g., role) of the groupings table 584 may refer togroupings of words, two or more word strings, an idiom, just identigens,just entigens, and/or any combination of the preceding elements. Eachentry has a unique grouping identifier. An idiom may have a uniquegrouping ID and include identifiers of original word identigens andreplacing identigens associated with the meaning of the idiom not justthe meaning of the original words. Valid groupings may still haveambiguity on their own and may need more strings and/or context toselect a best fit when interpreting a truest meaning of the grouping.

FIG. 8A is a data flow diagram for accumulating knowledge within acomputing system, where a computing device, at a time=tO, ingests andprocesses facts 598 at a step 590 based on rules 316 and fact baseinformation 600 to produce groupings 602 for storage in a fact base 592(e.g., words, phrases, word groupings, identigens, entigens, qualitylevels). The facts 598 may include information from books, archive data,Central intelligence agency (CIA) world fact book, trusted content, etc.The ingesting may include filtering to organize and promote better validgroupings detection (e.g., considering similar domains together). Thegroupings 602 includes one or more of groupings identifiers, identigenidentifiers, entigen identifiers, and estimated fit quality levels. Theprocessing step 590 may include identifying identigens from words of thefacts 598 in accordance with the rules 316 and the fact base info 600and identifying groupings utilizing identigens in accordance with rules316 and fact base info 600.

Subsequent to ingestion and processing of the facts 598 to establish thefact base 592, at a time=t1+, the computing device ingests and processesnew content 604 at a step 594 in accordance with the rules 316 and thefact base information 600 to produce preliminary grouping 606. The newcontent may include updated content (e.g., timewise) from periodicals,newsfeeds, social media, etc. The preliminary grouping 606 includes oneor more of preliminary groupings identifiers, preliminary identigenidentifiers, preliminary entigen identifiers, estimated fit qualitylevels, and representations of unidentified words.

The computing device validates the preliminary groupings 606 at a step596 based on the rules 316 and the fact base info 600 to produce updatedfact base info 608 for storage in the fact base 592. The validatingincludes one or more of reasoning a fit of existing fact base info 600with the new preliminary grouping 606, discarding preliminary groupings,updating just time frame information associated with an entry of theexisting fact base info 600 (e.g., to validate knowledge for thepresent), creating new entigens, and creating a median entigen tosummarize portions of knowledge within a median indicator as a qualitylevel indicator (e.g., suggestive not certain). Storage of the updatedfact base information 608 captures patterns that develop by themselvesinstead of searching for patterns as in prior art artificialintelligence systems. Growth of the fact base 592 enables subsequentreasoning to create new knowledge including deduction, induction,inference, and inferential sentiment (e.g., a chain of sentimentsentences). Examples of sentiments includes emotion, beliefs,convictions, feelings, judgments, notions, opinions, and views.

FIG. 8B is a diagram of an embodiment of a groupings table 620 within acomputing system. The groupings table 620 includes multiple fieldsincluding grouping ID 586, word strings 588, an IF string 622 and a THENstring 624. Each of the fields for the IF string 622 and the THEN string624 includes fields for an identigen (IDN) string 626, and an entigen(ENI) string 628. The computing system of the present invention mayproduce the groupings table 620 as a stored fact base to enable IF THENbased inference to generate a new knowledge inference 630.

As a specific example, grouping 5493 points out the logic of IF someonehas a tumor, THEN someone is sick and the grouping 5494 points of thelogic that IF someone is sick, THEN someone is sad. As a result ofutilizing inference, the new knowledge inference 630 may producegrouping 5495 where IF someone has a tumor, THEN someone is possibly sad(e.g., or is sad).

FIG. 8C is a data flow diagram for answering questions utilizingaccumulated knowledge within a computing system, where a computingdevice ingests and processes question information 346 at a step 640based on rules 316 and fact base info 600 from a fact base 592 toproduce preliminary grouping 606. The ingesting and processing questionsstep 640 includes identifying identigens from words of a question inaccordance with the rules 316 and the fact base information 600 and mayalso include identifying groupings from the identified identigens inaccordance with the rules 316 and the fact base information 600.

The computing device validates the preliminary grouping 606 at a step596 based on the rules 316 and the fact base information 600 to produceidentified element information 340. For example, the computing devicereasons fit of existing fact base information with new preliminarygroupings 606 to produce the identified element information 340associated with highest quality levels. The computing device interpretsa question of the identified element information 340 at a step 642 basedon the rules 316 and the fact base information 600. The interpreting ofthe question may include separating new content from the question andreducing the question based on the fact base information 600 and the newcontent.

The computing device produces preliminary answers 354 from theinterpreted information 344 at a resolve answer step 644 based on therules 316 and the fact base information 600. For example, the computingdevice compares the interpreted information 344 two the fact baseinformation 600 to produce the preliminary answers 354 with highestquality levels utilizing one or more of deduction, induction,inferencing, and applying inferential sentiments logic.

Alternatively, or in addition to, the computing device may save newknowledge identified from the question information 346 to update thefact base 592.

FIG. 8D is a data flow diagram for answering questions utilizinginterference within a computing system that includes a groupings table648 and the resolve answer step 644 of FIG. 8C. The groupings table 648includes multiple fields including fields for a grouping (GRP)identifier (ID) 586, word strings 588, an identigen (IDN) string 626,and an entigen (ENI) 628. The groupings table 648 may be utilized tobuild a fact base to enable resolving a future question into an answer.For example, the grouping 8356 notes knowledge that Michael sleeps eighthours and grouping 8357 notes that Michael usually starts to sleep at 11PM.

In a first question example that includes a question “Michaelsleeping?”, the resolve answer step 644 analyzes the question from theinterpreted information 344 in accordance with the fact base information600, the rules 316, and a real-time indicator that the current time is 1AM to produce a preliminary answer of “possibly YES” when inferring thatMichael is probably sleeping at 1 AM when Michael usually startssleeping at 11 PM and Michael usually sleeps for a duration of eighthours.

In a second question example that includes the question “Michaelsleeping?”, the resolve answer step 644 analyzes the question from theinterpreted information 344 in accordance with the fact base information600, the rules 316, and a real-time indicator that the current time isnow 11 AM to produce a preliminary answer of “possibly NO” wheninferring that Michael is probably not sleeping at 11 AM when Michaelusually starts sleeping at 11 PM and Michael usually sleeps for aduration of eight hours.

FIG. 8E is a relationship block diagram illustrating another embodimentof relationships between things and representations of things within acomputing system. While things in the real world are described withwords, it is often the case that a particular word has multiple meaningsin isolation. Interpreting the meaning of the particular word may hingeon analyzing how the word is utilized in a phrase, a sentence, multiplesentences, paragraphs, and even whole documents or more. Describing andstratifying the use of words, word types, and possible meanings help ininterpreting a true meaning.

Humans utilize textual words 528 to represent things in the real world.Quite often a particular word has multiple instances of differentgrammatical use when part of a phrase of one or more sentences. Thegrammatical use 649 of words includes the nouns and the verbs, and alsoincludes adverbs, adjectives, pronouns, conjunctions, prepositions,determiners, exclamations, etc.

As an example of multiple grammatical use, the word “bat” in the Englishlanguage can be utilized as a noun or a verb. For instance, whenutilized as a noun, the word “bat” may apply to a baseball bat or mayapply to a flying “bat.” As another instance, when utilized as a verb,the word “bat” may apply to the action of hitting or batting an object,i.e., “bat the ball.”

To stratify word types by use, the words are associated with a word type(e.g., type identifier 542). The word types include objects (e.g., items524), characteristics (e.g., attributes 526), actions 522, and thefunctionals 550 for joining other words and describing words. Forexample, when the word “bat” is utilized as a noun, the word isdescribing the object of either the baseball bat or the flying bat. Asanother example, when the word “bat” is utilized as a verb, the word isdescribing the action of hitting.

To determine possible meanings, the words, by word type, are mapped toassociative meanings (e.g., identigens 518). For each possibleassociative meaning, the word type is documented with the meaning andfurther with an identifier (ID) of the instance (e.g., an identigenidentifier).

For the example of the word “bat” when utilized as a noun for thebaseball bat, a first identigen identifier 536-1 includes a type ID542-1 associated with the object 524, an instance ID 540-1 associatedwith the first identigen identifier (e.g., unique for the baseball bat),and a meaning ID 538-1 associated with the baseball bat. For the exampleof the word “bat” when utilized as a noun for the flying bat, a secondidentigen identifier 536-2 includes a type ID 542-1 associated with theobject 524, an instance ID 540-2 associated with the second identigenidentifier (e.g., unique for the flying bat), and a meaning ID 538-2associated with the flying bat. For the example of the word “bat” whenutilized as a verb for the bat that hits, a third identigen identifier536-2 includes a type ID 542-2 associated with the actions 522, aninstance ID 540-3 associated with the third identigen identifier (e.g.,unique for the bat that hits), and a meaning ID 538-3 associated withthe bat that hits.

With the word described by a type and possible associative meanings, acombination of full grammatical use of the word within the phrase etc.,application of rules, and utilization of an ever-growing knowledgedatabase that represents knowledge by linked entigens, the absolutemeaning (e.g., entigen 520) of the word is represented as a uniqueentigen. For example, a first entigen e1 represents the absolute meaningof a baseball bat (e.g., a generic baseball bat not a particularbaseball bat that belongs to anyone), a second entigen e2 represents theabsolute meaning of the flying bat (e.g., a generic flying bat not aparticular flying bat), and a third entigen e3 represents the absolutemeaning of the verb bat (e.g., to hit).

An embodiment of methods to ingest text to produce absolute meanings forstorage in a knowledge database are discussed in greater detail withreference to FIGS. 8F-H. Those embodiments further discuss thediscerning of the grammatical use, the use of the rules, and theutilization of the knowledge database to definitively interpret theabsolute meaning of a string of words.

Another embodiment of methods to respond to a query to produce an answerbased on knowledge stored in the knowledge database are discussed ingreater detail with reference to FIGS. 8J-L. Those embodiments furtherdiscuss the discerning of the grammatical use, the use of the rules, andthe utilization of the knowledge database to interpret the query. Thequery interpretation is utilized to extract the answer from theknowledge database to facilitate forming the query response.

FIGS. 8F and 8G are schematic block diagrams of another embodiment of acomputing system that includes the content ingestion module 300 of FIG.5E, the element identification module 302 of FIG. 5E, the interpretationmodule 304 of FIG. 5E, the IEI control module 308 of FIG. 5E, and the SSmemory 96 of FIG. 2. Generally, an embodiment of this invention providespresents solutions where the computing system 10 supports processingcontent to produce knowledge for storage in a knowledge database.

The processing of the content to produce the knowledge includes a seriesof steps. For example, a first step includes identifying words of aningested phrase to produce tokenized words. As depicted in FIG. 8F, aspecific example of the first step includes the content ingestion module300 comparing words of source content 310 to dictionary entries toproduce formatted content 314 that includes identifiers of known words.Alternatively, when a comparison is unfavorable, the temporaryidentifier may be assigned to an unknown word. For instance, the contentingestion module 300 produces identifiers associated with the words“the”, “black”, “bat”, “eats”, and “fruit” when the ingested phraseincludes “The black bat eats fruit”, and generates the formatted content314 to include the identifiers of the words.

A second step of the processing of the content to produce the knowledgeincludes, for each tokenized word, identifying one or more identigensthat correspond the tokenized word, where each identigen describes oneof an object, a characteristic, and an action. As depicted in FIG. 8F, aspecific example of the second step includes the element identificationmodule 302 performing a look up of identigen identifiers, utilizing anelement list 332 and in accordance with element rules 318, of the one ormore identigens associated with each tokenized word of the formattedcontent 314 to produce identified element information 340.

A unique identifier is associated with each of the potential object, thecharacteristic, and the action (OCA) associated with the tokenized word(e.g. sequential identigens). For instance, the element identificationmodule 302 identifies a functional symbol for “the”, identifies a singleidentigen for “black”, identifies two identigens for “bat” (e.g.,baseball bat and flying bat), identifies a single identigen for “eats”,and identifies a single identigen for “fruit.” When at least onetokenized word is associated with multiple identigens, two or morepermutations of sequential combinations of identigens for each tokenizedword result. For example, when “bat” is associated with two identigens,two permutations of sequential combinations of identigens result for theingested phrase.

A third step of the processing of the content to produce the knowledgeincludes, for each permutation of sequential combinations of identigens,generating a corresponding equation package (i.e., candidateinterpretation), where the equation package includes a sequentiallinking of pairs of identigens (e.g., relationships), where eachsequential linking pairs a preceding identigen to a next identigen, andwhere an equation element describes a relationship between pairedidentigens (OCAs) such as describes, acts on, is a, belongs to, did, didto, etc. Multiple OCAs occur for a common word when the word hasmultiple potential meanings (e.g., a baseball bat, a flying bat).

As depicted in FIG. 8F, a specific example of the third step includesthe interpretation module 304, for each permutation of identigens ofeach tokenized word of the identified element information 340, theinterpretation module 304 generates, in accordance with interpretationrules 320 and a groupings list 334, an equation package to include oneor more of the identifiers of the tokenized words, a list of identifiersof the identigens of the equation package, a list of pairing identifiersfor sequential pairs of identigens, and a quality metric associated witheach sequential pair of identigens (e.g., likelihood of a properinterpretation). For instance, the interpretation module 304 produces afirst equation package that includes a first identigen pairing of ablack bat (e.g., flying bat with a higher quality metric level), thesecond pairing of bat eats (e.g., the flying bat eats, with a higherquality metric level), and a third pairing of eats fruit, and theinterpretation module 304 produces a second equation package thatincludes a first pairing of a black bat (e.g., baseball bat, with aneutral quality metric level), the second pairing of bat eats (e.g., thebaseball bat eats, with a lower quality metric level), and a thirdpairing of eats fruit.

A fourth step of the processing of the content to produce the knowledgeincludes selecting a surviving equation package associated with a mostfavorable confidence level. As depicted in FIG. 8F, a specific exampleof the fourth step includes the interpretation module 304 applyinginterpretation rules 320 (i.e., inference, pragmatic engine, utilizingthe identifiers of the identigens to match against known validcombinations of identifiers of entigens) to reduce a number ofpermutations of the sequential combinations of identigens to produceinterpreted information 344 that includes identification of at least oneequation package as a surviving interpretation SI (e.g., higher qualitymetric level).

Non-surviving equation packages are eliminated that compare unfavorablyto pairing rules and/or are associated with an unfavorable qualitymetric levels to produce a non-surviving interpretation NSI 2 (e.g.,lower quality metric level), where an overall quality metric level maybe assigned to each equation package based on quality metric levels ofeach pairing, such that a higher quality metric level of an equationpackage indicates a higher probability of a most favorableinterpretation. For instance, the interpretation module 304 eliminatesthe equation package that includes the second pairing indicating thatthe “baseball bat eats” which is inconsistent with a desired qualitymetric level of one or more of the groupings list 334 and theinterpretation rules 320 and selects the equation package associatedwith the “flying bat eats” which is favorably consistent with the one ormore of the quality metric levels of the groupings list 334 and theinterpretation rules 320.

A fifth step of the processing of the content to produce the knowledgeutilizing the confidence level includes integrating knowledge of thesurviving equation package into a knowledge database. For example,integrating at least a portion of the reduced OCA combinations into agraphical database to produce updated knowledge. As another example, theportion of the reduced OCA combinations may be translated into rows andcolumns entries when utilizing a rows and columns database rather than agraphical database. When utilizing the rows and columns approach for theknowledge database, subsequent access to the knowledge database mayutilize structured query language (SQL) queries.

As depicted in FIG. 8G, a specific example of the fifth step includesthe IEI control module 308 recovering fact base information 600 from SSmemory 96 to identify a portion of the knowledge database for potentialmodification utilizing the OCAs of the surviving interpretation SI 1(i.e., compare a pattern of relationships between the OCAs of thesurviving interpretation SI 1 from the interpreted information 344 torelationships of OCAs of the portion of the knowledge database includingpotentially new quality metric levels).

The fifth step further includes determining modifications (e.g.,additions, subtractions, further clarifications required wheninformation is complex, etc.) to the portion of the knowledge databasebased on the new quality metric levels. For instance, the IEI controlmodule 308 causes adding the element “black” as a “describes”relationship of an existing bat OCA and adding the element “fruit” as aneats “does to” relationship to implement the modifications to theportion of the fact base information 600 to produce updated fact baseinformation 608 for storage in the SS memory 96.

FIG. 8H is a logic diagram of an embodiment of a method for processingcontent to produce knowledge for storage within a computing system. Inparticular, a method is presented for use in conjunction with one ormore functions and features described in conjunction with FIGS. 1-8E,8F, and also FIG. 8G. The method includes step 650 where a processingmodule of one or more processing modules of one or more computingdevices of the computing system identifies words of an ingested phraseto produce tokenized words. The identified includes comparing words toknown words of dictionary entries to produce identifiers of known words.

For each tokenized word, the method continues at step 651 where theprocessing module identifies one or more identigens that corresponds tothe tokenized word, where each identigen describes one of an object, acharacteristic, and an action (e.g., OCA). The identifying includesperforming a lookup of identifiers of the one or more identigensassociated with each tokenized word, where the different identifiersassociated with each of the potential object, the characteristic, andthe action associated with the tokenized word.

The method continues at step 652 where the processing module, for eachpermutation of sequential combinations of identigens, generates aplurality of equation elements to form a corresponding equation package,where each equation element describes a relationship betweensequentially linked pairs of identigens, where each sequential linkingpairs a preceding identigen to a next identigen. For example, for eachpermutation of identigens of each tokenized word, the processing modulegenerates the equation package to include a plurality of equationelements, where each equation element describes the relationship (e.g.,describes, acts on, is a, belongs to, did, did too, etc.) betweensequentially adjacent identigens of a plurality of sequentialcombinations of identigens. Each equation element may be furtherassociated with a quality metric to evaluate a favorability level of aninterpretation in light of the sequence of identigens of the equationpackage.

The method continues at step 653 where the processing module selects asurviving equation package associated with most favorableinterpretation. For example, the processing module applies rules (i.e.,inference, pragmatic engine, utilizing the identifiers of the identigensto match against known valid combinations of identifiers of entigens),to reduce the number of permutations of the sequential combinations ofidentigens to identify at least one equation package, wherenon-surviving equation packages are eliminated the compare unfavorablyto pairing rules and/or are associated with an unfavorable qualitymetric levels to produce a non-surviving interpretation, where anoverall quality metric level is assigned to each equation package basedon quality metric levels of each pairing, such that a higher qualitymetric level indicates an equation package with a higher probability offavorability of correctness.

The method continues at step 654 where the processing module integratesknowledge of the surviving equation package into a knowledge database.For example, the processing module integrates at least a portion of thereduced OCA combinations into a graphical database to produce updatedknowledge. The integrating may include recovering fact base informationfrom storage of the knowledge database to identify a portion of theknowledge database for potential modifications utilizing the OCAs of thesurviving equation package (i.e., compare a pattern of relationshipsbetween the OCAs of the surviving equation package to relationships ofthe OCAs of the portion of the knowledge database including potentiallynew quality metric levels). The integrating further includes determiningmodifications (e.g., additions, subtractions, further clarificationsrequired when complex information is presented, etc.) to produce theupdated knowledge database that is based on fit of acceptable qualitymetric levels, and implementing the modifications to the portion of thefact base information to produce the updated fact base information forstorage in the portion of the knowledge database.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIGS. 8J and 8K are schematic block diagrams of another embodiment of acomputing system that includes the content ingestion module 300 of FIG.5E, the element identification module 302 of FIG. 5E, the interpretationmodule 304 of FIG. 5E, the answer resolution module 306 of FIG. 5E, andthe SS memory 96 of FIG. 2. Generally, an embodiment of this inventionprovides solutions where the computing system 10 supports for generatinga query response to a query utilizing a knowledge database.

The generating of the query response to the query includes a series ofsteps. For example, a first step includes identifying words of aningested query to produce tokenized words. As depicted in FIG. 8J, aspecific example of the first step includes the content ingestion module300 comparing words of query info 138 to dictionary entries to produceformatted content 314 that includes identifiers of known words. Forinstance, the content ingestion module 300 produces identifiers for eachword of the query “what black animal flies and eats fruit and insects?”

A second step of the generating of the query response to the queryincludes, for each tokenized word, identifying one or more identigensthat correspond the tokenized word, where each identigen describes oneof an object, a characteristic, and an action (OCA). As depicted in FIG.8J, a specific example of the second step includes the elementidentification module 302 performing a look up of identifiers, utilizingan element list 332 and in accordance with element rules 318, of the oneor more identigens associated with each tokenized word of the formattedcontent 314 to produce identified element information 340. A uniqueidentifier is associated with each of the potential object, thecharacteristic, and the action associated with a particular tokenizedword. For instance, the element identification module 302 produces asingle identigen identifier for each of the black color, an animal,flies, eats, fruit, and insects.

A third step of the generating of the query response to the queryincludes, for each permutation of sequential combinations of identigens,generating a corresponding equation package (i.e., candidateinterpretation). The equation package includes a sequential linking ofpairs of identigens, where each sequential linking pairs a precedingidentigen to a next identigen. An equation element describes arelationship between paired identigens (OCAs) such as describes, actson, is a, belongs to, did, did to, etc.

As depicted in FIG. 8J, a specific example of the third step includesthe interpretation module 304, for each permutation of identigens ofeach tokenized word of the identified element information 340,generating the equation packages in accordance with interpretation rules320 and a groupings list 334 to produce a series of equation elementsthat include pairings of identigens. For instance, the interpretationmodule 304 generates a first pairing to describe a black animal, asecond pairing to describe an animal that flies, a third pairing todescribe flies and eats, a fourth pairing to describe eats fruit, and afifth pairing to describe eats fruit and insects.

A fourth step of the generating the query response to the query includesselecting a surviving equation package associated with a most favorableinterpretation. As depicted in FIG. 8J, a specific example of the fourthstep includes the interpretation module 304 applying the interpretationrules 320 (i.e., inference, pragmatic engine, utilizing the identifiersof the identigens to match against known valid combinations ofidentifiers of entigens) to reduce the number of permutations of thesequential combinations of identigens to produce interpreted information344. The interpreted information 344 includes identification of at leastone equation package as a surviving interpretation SI 10, wherenon-surviving equation packages, if any, are eliminated that compareunfavorably to pairing rules to produce a non-surviving interpretation.

A fifth step of the generating the query response to the query includesutilizing a knowledge database, generating a query response to thesurviving equation package of the query, where the surviving equationpackage of the query is transformed to produce query knowledge forcomparison to a portion of the knowledge database. An answer isextracted from the portion of the knowledge database to produce thequery response.

As depicted in FIG. 8K, a specific example of the fifth step includesthe answer resolution module 306 interpreting the survivinginterpretation SI 10 of the interpreted information 344 in accordancewith answer rules 322 to produce query knowledge QK 10 (i.e., agraphical representation of knowledge when the knowledge databaseutilizes a graphical database). For example, the answer resolutionmodule 306 accesses fact base information 600 from the SS memory 96 toidentify the portion of the knowledge database associated with afavorable comparison of the query knowledge QK 10 (e.g., by comparingattributes of the query knowledge QK 10 to attributes of the fact baseinformation 600), and generates preliminary answers 354 that includesthe answer to the query. For instance, the answer is “bat” when theassociated OCAs of bat, such as black, eats fruit, eats insects, is ananimal, and flies, aligns with OCAs of the query knowledge.

FIG. 8L is a logic diagram of an embodiment of a method for generating aquery response to a query utilizing knowledge within a knowledgedatabase within a computing system. In particular, a method is presentedfor use in conjunction with one or more functions and features describedin conjunction with FIGS. 1-8D, 8J, and also FIG. 8K. The methodincludes step 655 where a processing module of one or more processingmodules of one or more computing devices of the computing systemidentifies words of an ingested query to produce tokenized words. Forexample, the processing module compares words to known words ofdictionary entries to produce identifiers of known words.

For each tokenized word, the method continues at step 656 where theprocessing module identifies one or more identigens that correspond tothe tokenized word, where each identigen describes one of an object, acharacteristic, and an action. For example, the processing moduleperforms a lookup of identifiers of the one or more identigensassociated with each tokenized word, where different identifiersassociated with each permutation of a potential object, characteristic,and action associated with the tokenized word.

For each permutation of sequential combinations of identigens, themethod continues at step 657 where the processing module generates aplurality of equation elements to form a corresponding equation package,where each equation element describes a relationship betweensequentially linked pairs of identigens. Each sequential linking pairs apreceding identigen to a next identigen. For example, for eachpermutation of identigens of each tokenized word, the processing moduleincludes all other permutations of all other tokenized words to generatethe equation packages. Each equation package includes a plurality ofequation elements describing the relationships between sequentiallyadjacent identigens of a plurality of sequential combinations ofidentigens.

The method continues at step 658 where the processing module selects asurviving equation package associated with a most favorableinterpretation. For example, the processing module applies rules (i.e.,inference, pragmatic engine, utilizing the identifiers of the identigensto match against known valid combinations of identifiers of entigens) toreduce the number of permutations of the sequential combinations ofidentigens to identify at least one equation package. Non-survivingequation packages are eliminated the compare unfavorably to pairingrules.

The method continues at step 659 where the processing module generates aquery response to the surviving equation package, where the survivingequation package is transformed to produce query knowledge for locatingthe portion of a knowledge database that includes an answer to thequery. As an example of generating the query response, the processingmodule interprets the surviving the equation package in accordance withanswer rules to produce the query knowledge (e.g., a graphicalrepresentation of knowledge when the knowledge database utilizes agraphical database format).

The processing module accesses fact base information from the knowledgedatabase to identify the portion of the knowledge database associatedwith a favorable comparison of the query knowledge (e.g., favorablecomparison of attributes of the query knowledge to the portion of theknowledge database, aligning favorably comparing entigens withoutconflicting entigens). The processing module extracts an answer from theportion of the knowledge database to produce the query response.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIGS. 9A and 9B are schematic block diagrams of another embodiment of acomputing system illustrating a method for converting content of a firstaptitude level to converted content of a second aptitude level withinthe computing system in accordance with the present invention. Thecomputing system includes the content ingestion module 300 of FIG. 5E,the element identification module 302 of FIG. 5E, the interpretationmodule 304 of FIG. 5E, the answer resolution module 306 of FIG. 5E, anda knowledge database 700. The knowledge database 700 may be implementedutilizing one or more of the memories of FIG. 2.

FIG. 9A illustrates an example of operation of steps of a method forconverting the content of the first aptitude level to the convertedcontent of the second aptitude level where the content ingestion module300 partitions a first aptitude level phrase (e.g., utilizing adictionary) to produce phrase words. For example, the content ingestionmodule 300 receives phrase 702 that includes the first aptitude levelphrase “black bats eat fruit” and partitions the phrase 702 to producephrase words 704 that includes the words “black”, “bats”, “eat”, and“fruit”.

The element identification module 302 identifies a set of identigens 708for each word of the first aptitude level phrase to produce a pluralityof sets of identigens. For example, the element identification module302 accesses, for each word of the phrase words 704, the knowledgedatabase 700 to retrieve an identigen set. For instance, the elementidentification module 302 receives identigen information 706 from theknowledge database 700 that includes an identigen set #1 for the word“black”, where the identigen set #1 includes identigens 1, 2, and 3. Theidentigens 1, 2, and 3 represent three unique interpretations of theword black, including “dark-skin people”, “black color”, and “to makeblack.”

The interpretation module 304 generates a first aptitude level entigengroup 712 for the first aptitude level phrase in accordance withidentigen rules 710. The first aptitude level entigen group 712represents a most likely interpretation of the first aptitude levelphrase. The first aptitude level phrase and the first aptitude levelentigen group are associated with a first aptitude level of a pluralityof aptitude levels.

The generating of the first aptitude level entigen group 712 includesinterpreting, utilizing the identigen rules 710, the plurality of setsof identigens 708 to produce the first aptitude level entigen group 712.A set of identigens of the plurality of sets of identigens includes oneor more different meanings of a word of the first aptitude level phrase.A first aptitude level entigen of the first aptitude level entigen groupcorresponds to an identigen of the set of identigens having a selectedmeaning of the one or more different meanings of the word of the firstaptitude level phrase. For example, the interpretation module 304identifies allowed pairings of identigens to include identigens 2 and 5,5 and 8, and 8 and 9 to produce the first aptitude level entigen group712 to include entigens 2, 5, 8, and 9. Alternatively, or in additionto, the interpretation module 304 identifies disallowed pairings ofidentigens (e.g., 1 and 4, 1 and 5, etc.) to produce the first aptitudelevel entigen group 712 that includes entigens 2, 5, 8, and 9.

FIG. 9B further illustrates the example of operation of steps of themethod for converting the content of the first aptitude level to theconverted content of the second aptitude level where the answerresolution module 306 obtains a multiple aptitude level entigen group714 from the knowledge database 700 based on the first aptitude levelentigen group 712. The multiple aptitude level entigen group 714substantially includes the first aptitude level entigen group 712. Themultiple aptitude level entigen group 714 is associated with theplurality of aptitude levels.

In an embodiment, higher levels of aptitude are associated with moreembellishment and more knowledge around connected meanings of themultiple aptitude level entigen group 714. The multiple aptitude levelentigen group 714 includes representations of entigens and connectorsthat represent relationships between the entigens. The connectorsdescribe relationships between the objects, actions, and characteristicentigens. The relationships include one or more of describes, acts on,is, is a, belongs to, did, did too, etc.

In an embodiment of the multiple aptitude level entigen group 714, afirst aptitude level includes knowledge that bats are black and eatfruit. In a second aptitude level, further knowledge represents bats canalso be Brown, are mammals, and also eat insects. In a third aptitudelevel, even further knowledge represents bats can be flying mammals.

The obtaining of the multiple aptitude level entigen group 714 from theknowledge database 700 based on the first aptitude level entigen group712 includes identifying a group of entigens of the knowledge database700 that compares favorably to the first aptitude level entigen group712 as the multiple aptitude level entigen group 714. A first entigen ofthe multiple aptitude level entigen group 714 is substantially the sameas a first entigen of the first aptitude level entigen group 712. Asecond entigen of the multiple aptitude level entigen group 714 issubstantially the same as a second entigen of the first aptitude levelentigen group 712. A first entigen relationship between the first andsecond entigens of the first aptitude level entigen group 712 issubstantially the same as a second entigen relationship between thefirst and second entigens of the multiple aptitude level entigen group714. For instance, the answer resolution module 306 receives entigeninformation 716 that includes entigens 2, 5, 8, and 9 of the multipleaptitude level entigen group 714 that matches the entigens of the firstaptitude level entigen group 712.

Alternatively, or in addition to, the answer resolution module 306identifies the first aptitude level based on an aptitude levelaffiliation of a set of entigens of the multiple aptitude level entigengroup that corresponds to the first aptitude level entigen group. Forexample, the answer resolution module 306 interprets the entigeninformation 716 to identify the entigens 2, 5, 8, and 9 of the multipleaptitude level entigen group 714 as associated with the first aptitudelevel.

Having obtained the multiple aptitude level entigen group 714, theanswer resolution module 306 generates a second aptitude level entigengroup 718 utilizing the multiple aptitude level entigen group 714. Thesecond aptitude level entigen group 718 is associated with a secondaptitude level of the plurality of aptitude levels. The second aptitudelevel is different than the first aptitude level. The generating of thesecond aptitude level entigen group 718 includes a series of steps.

A first step includes determining the second aptitude level. Thedetermining may be based on one or more of a higher aptitude level formore detail, a lower aptitude level for less detail, a particularentigen to be included or excluded, an entigen characteristic (i.e.,color, entigen type etc.), a number of characteristic combinations(i.e., include two colors, include two types of food, etc.). Forexample, the answer resolution module 306 determines the second aptitudelevel to include one or more level of detail.

A second step of the generating of the second aptitude level entigengroup 718 includes selecting entigens of the multiple aptitude levelentigen group 714 in accordance with the second aptitude level toproduce the second aptitude level entigen group. For example, the answerresolution module 306 interprets the entigen information 716 to furtherselect entigens 14, 20, and 30 when including the second aptitude levelto combine with entigens 2, 5, 8, and 9 to form the second aptitudelevel entigen group 718.

Having produced the second aptitude level entigen group 718, the answerresolution module 306 generates a second aptitude level phrase based onthe second aptitude level entigen group 718. The second aptitude levelentigen group represents a most likely interpretation of the secondaptitude level phrase. The second aptitude level phrase is associatedwith the second aptitude level.

The generating of the second aptitude level phrase includes selecting,for each entigen of the second aptitude level entigen group, a word(e.g., any language, from the knowledge database 700) associated withthe entigen of the second aptitude level entigen group to produce thesecond aptitude level phrase. For example, the answer resolution module306 outputs the second aptitude level phrase “bats are black or brownmammals that eat fruit and insects” when the second aptitude levelentigen group 718 includes entigens 2, 5, 8, 9, 14, 20, and 30. Asanother example, the answer resolution module 306 outputs a thirdaptitude level phrase “bats are black or brown flying mammals that eatfruit and insects” when a corresponding third aptitude level entigengroup includes entigens 2, 5, 8, 9, 14, 20, 30, and 50. In anotherexample, the answer resolution module 306 outputs a phrase of “blackbats eat fruit” when the input phrase 702 includes “bats are black orbrown flying mammals that eat fruit and insects” and a desired outputaptitude level is less than an input aptitude level of the phrase 702.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 10A is a schematic block diagram of another embodiment of acomputing system includes guidance content sources 750, the AI server20-1 of FIG. 1, and the user device 12-1 of FIG. 1. The guidance contentsources 750 includes the content sources 16-1 through 16-N of FIG. 1.The AI server 20-1 includes the processing module 50-1 of FIG. 2 and theSS memory 96 of FIG. 2. The processing module 50-1 includes the IEImodule 122 of FIG. 4A. Generally, an embodiment of this inventionpresents solutions where the computing system functions to formalizecontent.

In an example of operation of the formalizing the content the IEI module122 receives content for formalization. For example, the user device12-1 issues a query request 136 to the IEI module 122, where the queryrequest 136 includes received text from a speech to text converter.

Having received the content for formalization, the IEI module 122 IEIprocesses the content to produce input knowledge. For example, for eachword of a phrase of the content for formalization, the IEI module 122identifies a set of identigens, applies identigen sequencing rules toeliminate undesired permutations of identigens, and selects acorresponding entigen to produce an entigen group that represents theinput knowledge.

Having produced the input knowledge, the IEI module 122 obtainsexpression guidance for the content based on the input knowledge and aknowledge database that contains one or more formal rules for sentencestructure and punctuation and inferred rules based on observation oftextual expressions of knowledge. For example, the IEI module 122identifies that the content includes a question in obtains expressionguidance related to questions pertaining to general knowledge ofconnected meaning to the input knowledge (e.g., stored in the knowledgedatabase as fact base information 600 or gathered by issuing guidancecontent request 752 to the kinds content sources 750, and receivingguidance content responses 754 four further IEI processing to producethe expression guidance).

Having obtained the expression guidance, the IEI module 122 generatesformalize content utilizing the expression guidance and based on one ormore of the content of the input knowledge. For example, the IEI module22 rewrites the text to include a question mark at an appropriatelocation of the rewritten text. Having generated the formalize content,the IEI module 122 issues a query response 140 to the user device 12-1that includes the formalize content.

FIG. 10B is a data flow diagram of an embodiment of formalizing contentwithin a computing system. Input content 760 is IEI processed to produceinput knowledge 762. For example, an entigen group that includes theobject entigens bats and two or more color characteristic entigenplaceholders are generated when the input content 760 includes a seriesof words “what colors are bats”.

The input knowledge 762 is utilized to access a knowledge database toobtain expression guidance knowledge 764 where the expression guidanceknowledge 764 includes one or more expression entigen groups thatcompare favorably to the entigen group of the input knowledge 762. Theexpression entigen groups include objects and characteristics associatedwith the entigen group of the input knowledge 762. For example, a firstentigen group that identifies that dolphins have color characteristicsof black, gray, brown, and white is located as a first expression groupidentifying colors of an object. As another example, a second entigengroup that identifies bats with color characteristics and otherclarifying characteristic entigens is located as a second expressiongroup. In particular, the second entigen group indicates that bats areblack and brown and are mammals that can fly.

The input knowledge 762 and the expression guidance knowledge 764 areutilized to produce formalize content 766, where the formalize content766 includes punctuation and or additional clarifying and even leadingfacts associated with the input content 760. As a first example, theformalized content 766 includes a sentence “What colors are bats?” toresolve the missing punctuation of the input content 760 (e.g., toinclude the question mark).

As a second example, the formalized content 766 includes additionalclarifying information from the expression group 2 in the form of asentence “What colors are flying bats?” This distinguishes the questionfrom a question about baseball bats (e.g., not expressly shown butanticipated to be part of the expression guidance knowledge 764).

As a third example, the formalize content 766 includes additionalleading information from the expression group 2 in the form of anothersentence “Is it correct that flying bats are brown and black?” Thisdistinguishes the question from the question about baseball bats andprovides a leading answer to include knowledge from the expressionguidance knowledge 764 that bats are known to be brown and black ratherthan to state an open question.

FIG. 10C is a logic diagram of an embodiment of a method for formalizingcontent within a computing system. In particular, a method is presentedfor use in conjunction with one or more functions and features describedin conjunction with FIGS. 1-8L, and also FIGS. 10A-10B. The methodincludes step 780 where a processing module of one or more processingmodules of one or more computing devices of the computing systemreceives input content for formalization. For example, the processingmodule captures a live stream. As another example, the processing moduleextracts input content from a request to formalize the content. As yetanother example, the processing module accesses content from a contentstorage facility.

The method continues at step 782 where the processing module IEIprocesses the input content to produce input knowledge. For example, foreach word of a phrase of the input content, the processing moduleidentifies a set of identigens, applies identigen sequencing rules toeliminate undesired permutations of identigens, and selects acorresponding entigen to produce an entigen group that represents theinput knowledge.

The method continues at step 784 where the processing module obtainsexpression guidance for the input content based on the input knowledgeand a knowledge database. For example, the processing module matches atleast a portion of the input knowledge to a portion of the knowledgedatabase and identifies extensions of the input knowledge based on theportion of the knowledge database and/or expression sequencing andpunctuation guidance.

The method continues at step 786 where the processing module generatesformalize content for the input content utilizing the expressionguidance based on one or more of the input content and the inputknowledge. For example, the processing module rewrites the input contentand/or ads alternative knowledge for subsequent selection utilizing theexpression sequencing and punctuation guidance to update the inputcontent in accordance with the input knowledge and the portion of theknowledge database.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 11A is a schematic block diagram of another embodiment of acomputing system includes linking content sources 800, the AI server20-1 of FIG. 1, and the user device 12-1 of FIG. 1. The linking contentsources 800 includes the content sources 16-1 through 16-N of FIG. 1.The AI server 20-1 includes the processing module 50-1 of FIG. 2 and theSS memory 96 of FIG. 2. The processing module 50-1 includes the IEImodule 122 of FIG. 4A. Generally, an embodiment of this inventionpresents solutions where the computing system functions to access aknowledge database.

In an example of operation of the accessing the knowledge database, theIEI module 122 stores knowledge generated by IEI processing contentutilizing a knowledge database structure (e.g., a graphical database, arows and columns database). Database nodes typically represent entigensthat describe one of an object, a characteristic, or an action.Connectors between database nodes (e.g., entigens) representrelationships between entigens (e.g., acts on, is a, belongs to, did,did too, etc.). For example, the IEI module 122 receives linking contentresponses 804 from the linking content sources 800 in response toissuing one or more linking content requests 802 to the linking contentsources 800. The IEI module 122 IEI processes content of the linkingcontent responses 804 to produce the knowledge for storage in theknowledge database structure (e.g., as fact base information 600 in theSS memory 96).

Having stored knowledge in the knowledge database structure, the IEImodule 122 generates entigen index nodes that describe the relationshipbetween entigens of the database structure. For example, an index nodelinks two or more similar types of entigens (e.g., link actions, linksimilar characteristics, link objects) of clusters of entigens orbetween layers of the structure. The layers of the structure isdiscussed in greater detail with reference to FIG. 11B.

When accessing the knowledge database, the IEI module 122 accessesmultiple levels of index nodes to facilitate searching through thedatabase structure to find a desired cluster of entigens. For example,the IEI module 122 obtains fact base information 600 from the SS memory96 pertaining to index nodes that link the entigens of the desiredcluster of entigens.

FIG. 11B is structure diagram of an embodiment of a knowledge databasewithin a computing system. The knowledge database has a structure thatorganizes the knowledge database by levels. For example, a level 0includes an overall anchor root node and entigen root nodes by entigentype (e.g., objects, actions, and characteristics). The anchor root nodeof the entire knowledge database is organized by one or more domainssuch that another overall root node pertains to other domain(s).

An index node level 1 includes a variety of entigen clusters associatedwith different aspects of an associated root node and are linked tocorresponding root nodes of the index node level 0 by way of levelconnectors. Level connectors describe relationships between entigens byentigen type (object, characteristic, action, functional). For example,the index node level 0 root object node is linked to an entigen clusterassociated with people, another entigen cluster associated with places,and another entigen cluster associated with things. As another example,the index node level 0 root characteristic node is associated with anentigen cluster associated with colors, another entigen clusterassociated with sizes, and another entigen cluster associated with humanexpressions of emotion.

Entigen clusters of the index node level 1 are linked to entigen groups(e.g., leaf nodes) of an entigen group level. Entigen groups of theentigen group level include entigens that represent knowledge to besubsequently accessed (e.g., to generate a query response to a queryetc.).

When accessing the knowledge database (e.g., to service a query)utilizing the index nodes of the structure, the database may be enteredat the overall root node following a level connector to a root nodeassociated with an entigen type based on an attribute of the query.Having traversed to the root node by entigen type, another levelconnector is traversed to an entigen cluster associated with that aspectof the entigen type. For example, when the query is associated withgeographic locations, the traversal follows the level connector from theroot object node to the entigen cluster associated with places (e.g., inthe index node level 1). Further linking by connectors between entigensof the entigen cluster are utilized to follow another level connectorbetween an entigen of the entigen cluster to an entigen group at theentigen group level to further search for knowledge associated with thequery.

FIG. 11C is a logic diagram of an embodiment of a method for accessing aknowledge database within a computing system. In particular, a method ispresented for use in conjunction with one or more functions and featuresdescribed in conjunction with FIGS. 1-8L, and also FIGS. 11A-11B. Themethod includes step 830 where a processing module of one or moreprocessing modules of one or more computing devices of the computingsystem stores knowledge in a knowledge database structure, where contentis IEI processed to produce the knowledge, where the database structureincludes entigens and connectors that describe relationships between theentigens. For example, the processing module obtains content, IEIprocesses the content to produce the knowledge, stores representationsof entigens and representations of linkages between the entigens in theknowledge database (e.g., bulk knowledge stored at the entigen grouplevel). The method continues at step 832 where the processing moduleidentifies commonality of the entigens of the knowledge to produceentigen index nodes for storage in the knowledge database, where theentigen index notes describe a common relationship amongst linkedentigens. The identifying includes determining a type of entigen for theentigen (e.g., object, action, and characteristic), identifying asub-type for the entigen (e.g., when an object what type of logic, whenan action what type of action, when a characteristic what type ofcharacteristic, etc.). The processing module further identifies anentigen index node of the knowledge database associated with the typeand subtype, adds new entigen index nodes and/or links an existingentigen index node to entigens of the knowledge when storing theentigens of the knowledge in the knowledge database.

The method continues at step 834 where the processing module generates aquery entigen group for a query. For example, the processing module IEIprocesses the query to produce the query entigen group. The methodcontinues at step 836 for the processing module identifies a category ofentigen index node that compares favorably to the query entigen group.For example, the processing module determines type and sub-type for anentigen of the query entigen group and matches the type and sub-type ofthe entigen to a corresponding entigen index node of the knowledgedatabase.

The method continues at step 838 where the processing module accessesthe knowledge database by utilizing one or more of the entigen indexnodes associated with a category. For example, the processing modulesearches the knowledge database utilizing the corresponding entigenindex node to identify a portion of the knowledge database that comparesfavorably to the query entigen group (e.g., to identify an answer tosubsequent produce a query response).

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 12A is a schematic block diagram of another embodiment of acomputing system includes sorting content sources 850, the AI server20-1 of FIG. 1, and the user device 12-1 of FIG. 1. The sorting contentsources 850 includes the content sources 16-1 through 16-N of FIG. 1.The AI server 20-1 includes the processing module 50-1 of FIG. 2 and theSS memory 96 of FIG. 2. The processing module 50-1 includes the IEImodule 122 of FIG. 4A. Generally, an embodiment of this inventionpresents solutions where the computing system functions to access aknowledge database.

In an example of operation of the accessing the knowledge database, theIEI module 122 stores knowledge in a knowledge database structure,(e.g., as fact base information 600 in the SS memory 96. The IEI module122 IEI processes content to produce entigen clusters representing theknowledge. The knowledge database structure includes the entigenclusters and connectors that describe relationships between the entigensof each cluster. For example, the IEI module 122, in response to issuingsorting content requests 854 to the sorting content sources 850,receives sorting content responses 856. The IEI module 122 IEI processescontent of the sorting content responses 856 to produce knowledge forstorage as the entigen clusters in the SS memory 96.

The IEI module 122 links the entigen clusters via entigen index nodesassociated with each linked entigen cluster. The entigen index notesdescribe the common relationship amongst linked entigens of the entigenclusters. The IEI module 122 generates a query entigen group for aquery. For example, the IEI module 122 IEI processes query content froma query request 136 from the user device 12-1 to produce the queryentigen group.

Having produced the query entigen group, the IEI module 122 identifies acategory of entigen index node that compares favorably to the queryentigen group. For example, the IEI module 122 obtains fact baseinformation 600 from the SS memory 96 to identify the entigen indexnode.

Having identified the category, the IEI module 122 accesses theknowledge database by utilizing one or more of the entigen index nodesassociated with a category. For example, the IEI module 122 obtainsfurther fact base information 600 from the SS memory 96 to search by thecategory of the entigen index nodes to identify an entigen groupassociated with the query content. Having access the knowledge database,the WI module 122 generates a query response 144 sending to the userdevice 12-1, where the query response 140 includes one or more aspectsof the accessed knowledge database pertaining to the query.

FIG. 12B is a structure diagram of another embodiment of a knowledgedatabase within a computing system. The knowledge database has astructure that organizes the knowledge database by levels. For example,a level 0 includes an overall anchor root node and a series of rootnodes associated with any topic (e.g., to produce a very flat level 0structure). For instance, a root truck node is established at the indexnode level 0 to facilitate searching for knowledge related to trucks.The anchor root node of the entire knowledge database is organized byone or more domains such that another overall root node pertains toother domain(s).

An index node level 1 includes a variety of entigen clusters associatedwith different aspects of an associated root node and are linked tocorresponding root nodes of the index node level 0 by way of levelconnectors. Level connectors describe relationships between entigens byentigen type (object, characteristic, action, functional). For example,the index node level 0 root object node is linked to an entigen clusterassociated with pickups, another entigen cluster associated withflatbeds, and another entigen cluster associated with box trucks.

Entigen clusters of the index node level 1 are linked to entigen groups(e.g., leaf nodes) of an entigen group level. Entigen groups of theentigen group level include entigens that represent knowledge to besubsequently accessed (e.g., to generate a query response to a queryetc.). For example, pickups at index node level 1 are linked to a brandA entigen group at the entigen group level, flatbeds at index node level1 are linked to the brand A entigen group at the entigen group level andto a brand B entigen group at the entigen group level, etc.

When accessing the knowledge database (e.g., to service a query)utilizing the index nodes of the structure, the database may be enteredat the overall root node following a level connector to a root nodeassociated with a topic associated with the query. Another connector istraversed to an entigen cluster associated with an aspect of the topic.For example, when the query is associated with trucks, the traversalfollows the level connector from the root truck node to the entigencluster associated with flatbeds (e.g., in the index node level 1).Further linking by connectors between entigens of the entigen clusterare utilized to follow another level connector between an entigen of theentigen cluster for flatbeds to an entigen group at the entigen grouplevel to further search for knowledge associated with the query.

FIG. 12C is a logic diagram of another embodiment of a method foraccessing a knowledge database within a computing system. In particular,a method is presented for use in conjunction with one or more functionsand features described in conjunction with FIGS. 1-8L, and also FIGS.12A-12B. The method includes step 900 where a processing module of oneor more processing modules of one or more computing devices of thecomputing system stores knowledge in a knowledge database structure,where content is IEI processed to produce entigen clusters representingthe knowledge, where the knowledge database structure includes theentigen clusters and connectors that describe relationships between theentigens of each cluster. For example, the processing module obtains thecontent, IEI processes the content to produce the knowledge, and storesrepresentations of clusters of entigens of the knowledge and linkagesbetween the entigens in the knowledge database (e.g., leaf node level).

The method continues at step 902 where the processing module linksentigen clusters via entigen index nodes associated with each linkedentigen cluster, where the entigen index notes describe the commonrelationship amongst linked entigens of the entigen clusters. Forexample, the processing module determines a sorting type, adds a newentigen index node when no existing entigen index node comparesfavorably to the sorting type, and adds linkages between the entigensand/or entigen clusters that compare favorably to the sorting type.

The method continues at step 904 where the processing module generates aquery entigen group for a query. The processing module IEI processes thequery to produce the query entigen group.

The method continues at step 906 where the processing module identifiesa category of entigen index know the compares favorably to the queryentigen group. For example, the processing module determines a type andsub-type for an entigen of the query entigen group, matches the typeand/or sub-type of entigen to a corresponding entigen index node of theknowledge database.

The method continues at step 908 for the processing module accesses theknowledge database by utilizing one or more of the entigen index nodesassociated with a category. For example, the processing module searchesthe knowledge database utilizing the corresponding entigen index node toidentify a portion of the knowledge database that compares favorably tothe query entigen group (e.g., to identify an answer to subsequentproduce a query response).

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 13A is a schematic block diagram of another embodiment of acomputing system includes reference content sources 930, the AI server20-1 of FIG. 1, and the user device 12-1 of FIG. 1. The referencecontent sources 930 includes the content sources 16-1 through 16-N ofFIG. 1. The AI server 20-1 includes the processing module 50-1 of FIG. 2and the SS memory 96 of FIG. 2. The processing module 50-1 includes theIEI module 122 of FIG. 4A. Generally, an embodiment of this inventionpresents solutions where the computing system functions to correctingimpaired content.

In an example of operation of the correcting of the impaired content,the IEI module 122 receives impaired content that includes a series ofwords. The series of words is impaired by at least one of missing wordsand corrupted text. For example, a speech to text converter interpretsspeech incorrectly when the speech is mixed with noise from a noisybackground environment. As another example, transmission of correct textis corrupted during the transmission to produce the corrupted text. Forexample, the IEI module 122 extracts the impaired content from a contentperfection request 932 received from the user device 12-1. For instance,the impaired content pertains to an order entry process (e.g., items fororder).

The IEI module 122 IEI processes the impaired content to produce interimknowledge. For example, for each word of a phrase of the impairedcontent, the IEI module 122 identifies a set of identigens, appliesidentigen sequencing rules to eliminate undesired permutations ofidentigens, and selects a corresponding entigen to produce an entigengroup that represents the interim knowledge.

The IEI module 122 accesses a portion of a knowledge database thatcompares favorably to the interim knowledge. The IEI module 122identifies the portion of the knowledge database where at least some ofthe entigen group of the portion of the knowledge database aligns withthe entigen group that represents the interim knowledge.

The knowledge database is produced by storing fact base information 600and the SS memory 96 subsequent to IEI processing content of referencecontent responses 936 received by the reference content sources 930 toproduce knowledge for the knowledge database. The IEI module 122 issuesreference content request 934 to the reference content sources 932obtained content associated with references to bolster knowledge whenprocessing content for perfection. Having access the portion of theknowledge database, the IEI module 122 generates improved knowledgebased on the portion of the knowledge database. The improved knowledgeadds to and/or corrects knowledge associated with the impairment of theimpaired content. For example, the improved knowledge represents aviable order entry for the order entry system when the impaired contentrelates to an order for the order entry system.

Having generated the improved knowledge, the IEI module 122 outputs theimproved knowledge. For example, the outputting includes generating acontent perfection response 938 to include the improved knowledge and/orcorrected content from the improved knowledge, i.e., text that adds tothe series of words and/or corrects the series of words of the impairedcontent. Having generated the content perfection response 938, the IEImodule 122 sends the content perfection response 938 to the user device12-1.

As another example of operation, the IEI module 122 interprets aspeech-based autonomous vehicle speech to text destination input toaccurately identify the true destination. The generating of the improvedknowledge may be based on a multitude of references from the referencecontent sources 930, historical successful interpretations, languageutilization patterns, and from reference content about destinations.Another example is discussed in greater detail with reference to FIG.13B.

FIG. 13B is a data flow diagram of an embodiment for correcting impairedcontent within a computing system. Impaired content 950 is IEI processedto produce interim knowledge 952. The interim knowledge 952 includes aninterim knowledge entigen group that includes entigens and relationshipsbetween entigens associated with the impaired content. For example, theentigen group includes an object entigen bats, an action entigen eat,another object entigen fruit, and an open template for one or morecharacteristic entigens describing bats when the impaired content 950includes a series of words “Bats are xxxx and eat fruit.” The xxxxrepresents a missing word of the impairment.

The interim knowledge 952 is compared to entigen groups of a knowledgedatabase to identify a portion of the knowledge database 954 thecompares favorably to the interim knowledge 952. For example, an entigengroup with entigens of connected meaning to bats is located. The entigengroup includes characteristic entigens of black color, brown color, andflying mammals. The entigen group also includes entigens about eatingfruit and insects.

The portion of the knowledge database 954 and the interim knowledge 952are utilized to generate improved knowledge 956 to address theimpairment of the impaired content 950. For example, the missing worddescribes a characteristic of bats and the non-missing words indicatethat bats eat fruit. As a result, the improved knowledge 956 mustinclude all known characteristics of bats (e.g., black, brown, flyingmammals).

The improved knowledge 956 is utilized to produce corrected content 958.The corrected content 958 includes text of the impaired content 950 withadditions and/or corrections. For example, the corrected content 958includes a new sentence “Bats are black or brown mammals and eat fruit.”The corrected content 958 utilizes the knowledge from the portion of theknowledge database 954 to fill in for the missing word associated withcharacteristics of bats (e.g., they can be black or brown and aremammals).

FIG. 13C is a logic diagram of an embodiment of a method for correctingimpaired content within a computing system. In particular, a method ispresented for use in conjunction with one or more functions and featuresdescribed in conjunction with FIGS. 1-8L, and also FIGS. 13A-13B. Themethod includes step 970 where a processing module of one or moreprocessing modules of one or more computing devices of the computingsystem receives impaired content that includes a string of words subjectto one or more impairments (e.g., missing words, corrupted words). Forexample, the processing module extracts the string of words from arequest to correct potentially impaired content.

The method continues at step 972 where the processing module IEIprocesses the impaired content to produce interim knowledge. Forexample, for each word of the string of words of the impaired content,the IEI module 122 identifies a set of identigens, applies identigensequencing rules to eliminate undesired permutations of identigens, andselects a corresponding entigen to produce an entigen group thatrepresents the interim knowledge. Alternatively, or in addition to, theprocessing module identifies one or more positions associated with amissing word based on the identigen pairing rules.

The method continues at step 974 where the processing module accesses aportion of a knowledge database that compares favorably to the interimknowledge. For example, the processing module identifies a portion ofthe knowledge database that includes an entigen group where the entigenscompare favorably to the entigens of the interim knowledge.

The method continues at step 976 where the processing module generatesimproved knowledge utilizing the portion of the knowledge database. Forexample, the processing module augments the interim knowledge withadditional knowledge from the portion of the knowledge database ofconnected meaning where the augmentation fills in for potentiallymissing knowledge of the interim knowledge. For example, filling incharacteristics of bats when the impaired content is related to bats andis associated with characteristics of bats.

The method continues at step 978 the processing module outputs arepresentation of the improved knowledge. The representation of improvedknowledge includes the improved knowledge and updated text representingcorrected content. For example, the processing module modifies theimpaired content with additional word corrections and/or additionalwords associated with a desired language in accordance with identigensequencing rules based on the improved knowledge.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a computing device, themethod comprises: generating a first aptitude level entigen group for afirst aptitude level phrase in accordance with identigen rules, whereinthe first aptitude level entigen group represents a most likelyinterpretation of the first aptitude level phrase, wherein the firstaptitude level phrase and the first aptitude level entigen group areassociated with a first aptitude level of a plurality of aptitudelevels; obtaining a multiple aptitude level entigen group from aknowledge database based on the first aptitude level entigen group,wherein the multiple aptitude level entigen group substantially includesthe first aptitude level entigen group, wherein the multiple aptitudelevel entigen group is associated with the plurality of aptitude levels;generating a second aptitude level entigen group utilizing the multipleaptitude level entigen group, wherein the second aptitude level entigengroup is associated with a second aptitude level of the plurality ofaptitude levels, wherein the second aptitude level is different than thefirst aptitude level; and generating a second aptitude level phrasebased on the second aptitude level entigen group, wherein the secondaptitude level entigen group represents a most likely interpretation ofthe second aptitude level phrase, and wherein the second aptitude levelphrase is associated with the second aptitude level.
 2. The method ofclaim 1 further comprises: identifying the first aptitude level based onan aptitude level affiliation of a set of entigens of the multipleaptitude level entigen group that corresponds to the first aptitudelevel entigen group.
 3. The method of claim 1, wherein the generatingthe first aptitude level entigen group for the first aptitude levelphrase in accordance with the identigen rules comprises: identifying aset of identigens for each word of the first aptitude level phrase toproduce a plurality of sets of identigens; and interpreting, utilizingthe identigen rules, the plurality of sets of identigens to produce thefirst aptitude level entigen group, wherein a set of identigens of theplurality of sets of identigens includes one or more different meaningsof a word of the first aptitude level phrase, wherein a first aptitudelevel entigen of the first aptitude level entigen group corresponds toan identigen of the set of identigens having a selected meaning of theone or more different meanings of the word of the first aptitude levelphrase.
 4. The method of claim 1, wherein the obtaining the multipleaptitude level entigen group from the knowledge database based on thefirst aptitude level entigen group comprises: identifying a group ofentigens of the knowledge database that compares favorably to the firstaptitude level entigen group as the multiple aptitude level entigengroup, wherein a first entigen of the multiple aptitude level entigengroup is substantially the same as a first entigen of the first aptitudelevel entigen group, wherein a second entigen of the multiple aptitudelevel entigen group is substantially the same as a second entigen of thefirst aptitude level entigen group, and wherein a first entigenrelationship between the first and second entigens of the first aptitudelevel entigen group is substantially the same as a second entigenrelationship between the first and second entigens of the multipleaptitude level entigen group.
 5. The method of claim 1, wherein thegenerating the second aptitude level entigen group utilizing themultiple aptitude level entigen group comprises: determining the secondaptitude level; and selecting entigens of the multiple aptitude levelentigen group in accordance with the second aptitude level to producethe second aptitude level entigen group.
 6. The method of claim 1,wherein the generating the second aptitude level phrase based on thesecond aptitude level entigen group comprises: selecting, for eachentigen of the second aptitude level entigen group, a word associatedwith the entigen of the second aptitude level entigen group to producethe second aptitude level phrase.
 7. A computing device of a computingsystem, the computing device comprises: an interface; a local memory;and a processing module operably coupled to the interface and the localmemory, wherein the processing module functions to: generate a firstaptitude level entigen group for a first aptitude level phrase inaccordance with identigen rules, wherein the first aptitude levelentigen group represents a most likely interpretation of the firstaptitude level phrase, wherein the first aptitude level phrase and thefirst aptitude level entigen group are associated with a first aptitudelevel of a plurality of aptitude levels; obtain a multiple aptitudelevel entigen group from a knowledge database based on the firstaptitude level entigen group, wherein the multiple aptitude levelentigen group substantially includes the first aptitude level entigengroup, wherein the multiple aptitude level entigen group is associatedwith the plurality of aptitude levels; generate a second aptitude levelentigen group utilizing the multiple aptitude level entigen group,wherein the second aptitude level entigen group is associated with asecond aptitude level of the plurality of aptitude levels, wherein thesecond aptitude level is different than the first aptitude level; andgenerate a second aptitude level phrase based on the second aptitudelevel entigen group, wherein the second aptitude level entigen grouprepresents a most likely interpretation of the second aptitude levelphrase, and wherein the second aptitude level phrase is associated withthe second aptitude level.
 8. The computing device of claim 7, whereinthe processing module further functions to: identify the first aptitudelevel based on an aptitude level affiliation of a set of entigens of themultiple aptitude level entigen group that corresponds to the firstaptitude level entigen group.
 9. The computing device of claim 7,wherein the processing module functions to generate the first aptitudelevel entigen group for the first aptitude level phrase in accordancewith the identigen rules by: identifying a set of identigens for eachword of the first aptitude level phrase to produce a plurality of setsof identigens; and interpreting, utilizing the identigen rules, theplurality of sets of identigens to produce the first aptitude levelentigen group, wherein a set of identigens of the plurality of sets ofidentigens includes one or more different meanings of a word of thefirst aptitude level phrase, wherein a first aptitude level entigen ofthe first aptitude level entigen group corresponds to an identigen ofthe set of identigens having a selected meaning of the one or moredifferent meanings of the word of the first aptitude level phrase. 10.The computing device of claim 7, wherein the processing module functionsto obtain the multiple aptitude level entigen group from the knowledgedatabase based on the first aptitude level entigen group by: identifyinga group of entigens of the knowledge database that compares favorably tothe first aptitude level entigen group as the multiple aptitude levelentigen group, wherein a first entigen of the multiple aptitude levelentigen group is substantially the same as a first entigen of the firstaptitude level entigen group, wherein a second entigen of the multipleaptitude level entigen group is substantially the same as a secondentigen of the first aptitude level entigen group, and wherein a firstentigen relationship between the first and second entigens of the firstaptitude level entigen group is substantially the same as a secondentigen relationship between the first and second entigens of themultiple aptitude level entigen group.
 11. The computing device of claim7, wherein the processing module functions to generate the secondaptitude level entigen group utilizing the multiple aptitude levelentigen group by: determining the second aptitude level; and selectingentigens of the multiple aptitude level entigen group in accordance withthe second aptitude level to produce the second aptitude level entigengroup.
 12. The computing device of claim 7, wherein the processingmodule functions to generate the second aptitude level phrase based onthe second aptitude level entigen group by: selecting, for each entigenof the second aptitude level entigen group, a word associated with theentigen of the second aptitude level entigen group to produce the secondaptitude level phrase.
 13. A computer readable memory comprises: a firstmemory element that stores operational instructions that, when executedby a processing module, causes the processing module to: generate afirst aptitude level entigen group for a first aptitude level phrase inaccordance with identigen rules, wherein the first aptitude levelentigen group represents a most likely interpretation of the firstaptitude level phrase, wherein the first aptitude level phrase and thefirst aptitude level entigen group are associated with a first aptitudelevel of a plurality of aptitude levels; a second memory element thatstores operational instructions that, when executed by the processingmodule, causes the processing module to: obtain a multiple aptitudelevel entigen group from a knowledge database based on the firstaptitude level entigen group, wherein the multiple aptitude levelentigen group substantially includes the first aptitude level entigengroup, wherein the multiple aptitude level entigen group is associatedwith the plurality of aptitude levels; a third memory element thatstores operational instructions that, when executed by the processingmodule, causes the processing module to: generate a second aptitudelevel entigen group utilizing the multiple aptitude level entigen group,wherein the second aptitude level entigen group is associated with asecond aptitude level of the plurality of aptitude levels, wherein thesecond aptitude level is different than the first aptitude level; and afourth memory element that stores operational instructions that, whenexecuted by the processing module, causes the processing module to:generate a second aptitude level phrase based on the second aptitudelevel entigen group, wherein the second aptitude level entigen grouprepresents a most likely interpretation of the second aptitude levelphrase, and wherein the second aptitude level phrase is associated withthe second aptitude level.
 14. The computer readable memory of claim 13further comprises: a fifth memory element that stores operationalinstructions that, when executed by the processing module, causes theprocessing module to: identify the first aptitude level based on anaptitude level affiliation of a set of entigens of the multiple aptitudelevel entigen group that corresponds to the first aptitude level entigengroup.
 15. The computer readable memory of claim 13, wherein theprocessing module functions to execute the operational instructionsstored by the first memory element to cause the processing module togenerate the first aptitude level entigen group for the first aptitudelevel phrase in accordance with the identigen rules by: identifying aset of identigens for each word of the first aptitude level phrase toproduce a plurality of sets of identigens; and interpreting, utilizingthe identigen rules, the plurality of sets of identigens to produce thefirst aptitude level entigen group, wherein a set of identigens of theplurality of sets of identigens includes one or more different meaningsof a word of the first aptitude level phrase, wherein a first aptitudelevel entigen of the first aptitude level entigen group corresponds toan identigen of the set of identigens having a selected meaning of theone or more different meanings of the word of the first aptitude levelphrase.
 16. The computer readable memory of claim 13, wherein theprocessing module functions to execute the operational instructionsstored by the second memory element to cause the processing module toobtain the multiple aptitude level entigen group from the knowledgedatabase based on the first aptitude level entigen group by: identifyinga group of entigens of the knowledge database that compares favorably tothe first aptitude level entigen group as the multiple aptitude levelentigen group, wherein a first entigen of the multiple aptitude levelentigen group is substantially the same as a first entigen of the firstaptitude level entigen group, wherein a second entigen of the multipleaptitude level entigen group is substantially the same as a secondentigen of the first aptitude level entigen group, and wherein a firstentigen relationship between the first and second entigens of the firstaptitude level entigen group is substantially the same as a secondentigen relationship between the first and second entigens of themultiple aptitude level entigen group.
 17. The computer readable memoryof claim 13, wherein the processing module functions to execute theoperational instructions stored by the third memory element to cause theprocessing module to generate the second aptitude level entigen grouputilizing the multiple aptitude level entigen group by: determining thesecond aptitude level; and selecting entigens of the multiple aptitudelevel entigen group in accordance with the second aptitude level toproduce the second aptitude level entigen group.
 18. The computerreadable memory of claim 13, wherein the processing module functions toexecute the operational instructions stored by the fourth memory elementto cause the processing module to generate the second aptitude levelphrase based on the second aptitude level entigen group by: selecting,for each entigen of the second aptitude level entigen group, a wordassociated with the entigen of the second aptitude level entigen groupto produce the second aptitude level phrase.